Scikits-Learn.ipynb

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      "#Classic Machine Learning Example: Iris Data Set\n",
      "\n",
      "## Measurements or Features\n",
      "* sepal length in cm\n",
      "* sepal width in cm\n",
      "* petal length in cm\n",
      "* petal width in cm\n",
      "\n",
      "## Type or Classes\n",
      "* Iris Setosa\n",
      "* Iris Versicolour\n",
      "* Iris Virginica"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.datasets as ds\n",
      "help(ds)"
     ],
     "language": "python",
     "metadata": {},
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       "stream": "stdout",
       "text": [
        "Help on package sklearn.datasets in sklearn:\n",
        "\n",
        "NAME\n",
        "    sklearn.datasets\n",
        "\n",
        "FILE\n",
        "    /Users/quasiben/Research/ContinuumDev/Anacondas/INSTALL/lib/python2.7/site-packages/sklearn/datasets/__init__.py\n",
        "\n",
        "DESCRIPTION\n",
        "    The :mod:`sklearn.datasets` module includes utilities to load datasets,\n",
        "    including methods to load and fetch popular reference datasets. It also\n",
        "    features some artificial data generators.\n",
        "\n",
        "PACKAGE CONTENTS\n",
        "    _svmlight_format\n",
        "    base\n",
        "    california_housing\n",
        "    lfw\n",
        "    mlcomp\n",
        "    mldata\n",
        "    olivetti_faces\n",
        "    samples_generator\n",
        "    setup\n",
        "    species_distributions\n",
        "    svmlight_format\n",
        "    tests (package)\n",
        "    twenty_newsgroups\n",
        "\n",
        "FUNCTIONS\n",
        "    clear_data_home(data_home=None)\n",
        "        Delete all the content of the data home cache.\n",
        "    \n",
        "    dump_svmlight_file(X, y, f, zero_based=True, comment=None, query_id=None)\n",
        "        Dump the dataset in svmlight / libsvm file format.\n",
        "        \n",
        "        This format is a text-based format, with one sample per line. It does\n",
        "        not store zero valued features hence is suitable for sparse dataset.\n",
        "        \n",
        "        The first element of each line can be used to store a target variable\n",
        "        to predict.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
        "            Training vectors, where n_samples is the number of samples and\n",
        "            n_features is the number of features.\n",
        "        \n",
        "        y : array-like, shape = [n_samples]\n",
        "            Target values.\n",
        "        \n",
        "        f : string or file-like in binary mode\n",
        "            If string, specifies the path that will contain the data.\n",
        "            If file-like, data will be written to f. f should be opened in binary\n",
        "            mode.\n",
        "        \n",
        "        zero_based : boolean, optional\n",
        "            Whether column indices should be written zero-based (True) or one-based\n",
        "            (False).\n",
        "        \n",
        "        comment : string, optional\n",
        "            Comment to insert at the top of the file. This should be either a\n",
        "            Unicode string, which will be encoded as UTF-8, or an ASCII byte\n",
        "            string.\n",
        "            If a comment is given, then it will be preceded by one that identifies\n",
        "            the file as having been dumped by scikit-learn. Note that not all\n",
        "            tools grok comments in SVMlight files.\n",
        "        \n",
        "        query_id : array-like, shape = [n_samples]\n",
        "            Array containing pairwise preference constraints (qid in svmlight\n",
        "            format).\n",
        "    \n",
        "    fetch_20newsgroups(data_home=None, subset='train', categories=None, shuffle=True, random_state=42, download_if_missing=True)\n",
        "        Load the filenames of the 20 newsgroups dataset.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        subset: 'train' or 'test', 'all', optional\n",
        "            Select the dataset to load: 'train' for the training set, 'test'\n",
        "            for the test set, 'all' for both, with shuffled ordering.\n",
        "        \n",
        "        data_home: optional, default: None\n",
        "            Specify an download and cache folder for the datasets. If None,\n",
        "            all scikit-learn data is stored in '~/scikit_learn_data' subfolders.\n",
        "        \n",
        "        categories: None or collection of string or unicode\n",
        "            If None (default), load all the categories.\n",
        "            If not None, list of category names to load (other categories\n",
        "            ignored).\n",
        "        \n",
        "        shuffle: bool, optional\n",
        "            Whether or not to shuffle the data: might be important for models that\n",
        "            make the assumption that the samples are independent and identically\n",
        "            distributed (i.i.d.), such as stochastic gradient descent.\n",
        "        \n",
        "        random_state: numpy random number generator or seed integer\n",
        "            Used to shuffle the dataset.\n",
        "        \n",
        "        download_if_missing: optional, True by default\n",
        "            If False, raise an IOError if the data is not locally available\n",
        "            instead of trying to download the data from the source site.\n",
        "    \n",
        "    fetch_20newsgroups_vectorized(subset='train', data_home=None)\n",
        "        Load the 20 newsgroups dataset and transform it into tf-idf vectors.\n",
        "        \n",
        "        This is a convenience function; the tf-idf transformation is done using the\n",
        "        default settings for `sklearn.feature_extraction.text.Vectorizer`. For more\n",
        "        advanced usage (stopword filtering, n-gram extraction, etc.), combine\n",
        "        fetch_20newsgroups with a custom `Vectorizer` or `CountVectorizer`.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        \n",
        "        subset: 'train' or 'test', 'all', optional\n",
        "            Select the dataset to load: 'train' for the training set, 'test'\n",
        "            for the test set, 'all' for both, with shuffled ordering.\n",
        "        \n",
        "        data_home: optional, default: None\n",
        "            Specify an download and cache folder for the datasets. If None,\n",
        "            all scikit-learn data is stored in '~/scikit_learn_data' subfolders.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        \n",
        "        bunch : Bunch object\n",
        "            bunch.data: sparse matrix, shape [n_samples, n_features]\n",
        "            bunch.target: array, shape [n_samples]\n",
        "            bunch.target_names: list, length [n_classes]\n",
        "    \n",
        "    fetch_california_housing(data_home=None, download_if_missing=True)\n",
        "        Loader for the California housing dataset from StatLib.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        data_home : optional, default: None\n",
        "            Specify another download and cache folder for the datasets. By default\n",
        "            all scikit learn data is stored in '~/scikit_learn_data' subfolders.\n",
        "        \n",
        "        download_if_missing: optional, True by default\n",
        "            If False, raise a IOError if the data is not locally available\n",
        "            instead of trying to download the data from the source site.\n",
        "        \n",
        "        Notes\n",
        "        ------\n",
        "        \n",
        "        This dataset consists of 20,640 samples and 9 features.\n",
        "    \n",
        "    fetch_lfw_pairs(subset='train', data_home=None, funneled=True, resize=0.5, color=False, slice_=(slice(70, 195, None), slice(78, 172, None)), download_if_missing=True)\n",
        "        Loader for the Labeled Faces in the Wild (LFW) pairs dataset\n",
        "        \n",
        "        This dataset is a collection of JPEG pictures of famous people\n",
        "        collected on the internet, all details are available on the\n",
        "        official website:\n",
        "        \n",
        "            http://vis-www.cs.umass.edu/lfw/\n",
        "        \n",
        "        Each picture is centered on a single face. Each pixel of each channel\n",
        "        (color in RGB) is encoded by a float in range 0.0 - 1.0.\n",
        "        \n",
        "        The task is called Face Verification: given a pair of two pictures,\n",
        "        a binary classifier must predict whether the two images are from\n",
        "        the same person.\n",
        "        \n",
        "        In the official `README.txt`_ this task is described as the\n",
        "        \"Restricted\" task.  As I am not sure as to implement the\n",
        "        \"Unrestricted\" variant correctly, I left it as unsupported for now.\n",
        "        \n",
        "          .. _`README.txt`: http://vis-www.cs.umass.edu/lfw/README.txt\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        subset: optional, default: 'train'\n",
        "            Select the dataset to load: 'train' for the development training\n",
        "            set, 'test' for the development test set, and '10_folds' for the\n",
        "            official evaluation set that is meant to be used with a 10-folds\n",
        "            cross validation.\n",
        "        \n",
        "        data_home: optional, default: None\n",
        "            Specify another download and cache folder for the datasets. By\n",
        "            default all scikit learn data is stored in '~/scikit_learn_data'\n",
        "            subfolders.\n",
        "        \n",
        "        funneled: boolean, optional, default: True\n",
        "            Download and use the funneled variant of the dataset.\n",
        "        \n",
        "        resize: float, optional, default 0.5\n",
        "            Ratio used to resize the each face picture.\n",
        "        \n",
        "        color: boolean, optional, default False\n",
        "            Keep the 3 RGB channels instead of averaging them to a single\n",
        "            gray level channel. If color is True the shape of the data has\n",
        "            one more dimension than than the shape with color = False.\n",
        "        \n",
        "        slice_: optional\n",
        "            Provide a custom 2D slice (height, width) to extract the\n",
        "            'interesting' part of the jpeg files and avoid use statistical\n",
        "            correlation from the background\n",
        "        \n",
        "        download_if_missing: optional, True by default\n",
        "            If False, raise a IOError if the data is not locally available\n",
        "            instead of trying to download the data from the source site.\n",
        "    \n",
        "    fetch_lfw_people(data_home=None, funneled=True, resize=0.5, min_faces_per_person=None, color=False, slice_=(slice(70, 195, None), slice(78, 172, None)), download_if_missing=True)\n",
        "        Loader for the Labeled Faces in the Wild (LFW) people dataset\n",
        "        \n",
        "        This dataset is a collection of JPEG pictures of famous people\n",
        "        collected on the internet, all details are available on the\n",
        "        official website:\n",
        "        \n",
        "            http://vis-www.cs.umass.edu/lfw/\n",
        "        \n",
        "        Each picture is centered on a single face. Each pixel of each channel\n",
        "        (color in RGB) is encoded by a float in range 0.0 - 1.0.\n",
        "        \n",
        "        The task is called Face Recognition (or Identification): given the\n",
        "        picture of a face, find the name of the person given a training set\n",
        "        (gallery).\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        data_home: optional, default: None\n",
        "            Specify another download and cache folder for the datasets. By default\n",
        "            all scikit learn data is stored in '~/scikit_learn_data' subfolders.\n",
        "        \n",
        "        funneled: boolean, optional, default: True\n",
        "            Download and use the funneled variant of the dataset.\n",
        "        \n",
        "        resize: float, optional, default 0.5\n",
        "            Ratio used to resize the each face picture.\n",
        "        \n",
        "        min_faces_per_person: int, optional, default None\n",
        "            The extracted dataset will only retain pictures of people that have at\n",
        "            least `min_faces_per_person` different pictures.\n",
        "        \n",
        "        color: boolean, optional, default False\n",
        "            Keep the 3 RGB channels instead of averaging them to a single\n",
        "            gray level channel. If color is True the shape of the data has\n",
        "            one more dimension than than the shape with color = False.\n",
        "        \n",
        "        slice_: optional\n",
        "            Provide a custom 2D slice (height, width) to extract the\n",
        "            'interesting' part of the jpeg files and avoid use statistical\n",
        "            correlation from the background\n",
        "        \n",
        "        download_if_missing: optional, True by default\n",
        "            If False, raise a IOError if the data is not locally available\n",
        "            instead of trying to download the data from the source site.\n",
        "    \n",
        "    fetch_mldata(dataname, target_name='label', data_name='data', transpose_data=True, data_home=None)\n",
        "        Fetch an mldata.org data set\n",
        "        \n",
        "        If the file does not exist yet, it is downloaded from mldata.org .\n",
        "        \n",
        "        mldata.org does not have an enforced convention for storing data or\n",
        "        naming the columns in a data set. The default behavior of this function\n",
        "        works well with the most common cases:\n",
        "        \n",
        "          1) data values are stored in the column 'data', and target values in the\n",
        "             column 'label'\n",
        "          2) alternatively, the first column stores target values, and the second\n",
        "             data values\n",
        "          3) the data array is stored as `n_features x n_samples` , and thus needs\n",
        "             to be transposed to match the `sklearn` standard\n",
        "        \n",
        "        Keyword arguments allow to adapt these defaults to specific data sets\n",
        "        (see parameters `target_name`, `data_name`, `transpose_data`, and\n",
        "        the examples below).\n",
        "        \n",
        "        mldata.org data sets may have multiple columns, which are stored in the\n",
        "        Bunch object with their original name.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        \n",
        "        dataname:\n",
        "            Name of the data set on mldata.org,\n",
        "            e.g.: \"leukemia\", \"Whistler Daily Snowfall\", etc.\n",
        "            The raw name is automatically converted to a mldata.org URL .\n",
        "        \n",
        "        target_name: optional, default: 'label'\n",
        "            Name or index of the column containing the target values.\n",
        "        \n",
        "        data_name: optional, default: 'data'\n",
        "            Name or index of the column containing the data.\n",
        "        \n",
        "        transpose_data: optional, default: True\n",
        "            If True, transpose the downloaded data array.\n",
        "        \n",
        "        data_home: optional, default: None\n",
        "            Specify another download and cache folder for the data sets. By default\n",
        "            all scikit learn data is stored in '~/scikit_learn_data' subfolders.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        \n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are:\n",
        "            'data', the data to learn, 'target', the classification labels,\n",
        "            'DESCR', the full description of the dataset, and\n",
        "            'COL_NAMES', the original names of the dataset columns.\n",
        "        \n",
        "        Examples\n",
        "        --------\n",
        "        Load the 'iris' dataset from mldata.org:\n",
        "        >>> from sklearn.datasets.mldata import fetch_mldata\n",
        "        >>> iris = fetch_mldata('iris')\n",
        "        >>> iris.target[0]\n",
        "        1\n",
        "        >>> print(iris.data[0])\n",
        "        [-0.555556  0.25     -0.864407 -0.916667]\n",
        "        \n",
        "        Load the 'leukemia' dataset from mldata.org, which needs to be transposed\n",
        "        to respects the sklearn axes convention:\n",
        "        >>> leuk = fetch_mldata('leukemia', transpose_data=True)\n",
        "        >>> print(leuk.data.shape[0])\n",
        "        72\n",
        "        \n",
        "        Load an alternative 'iris' dataset, which has different names for the\n",
        "        columns:\n",
        "        >>> iris2 = fetch_mldata('datasets-UCI iris', target_name=1,\n",
        "        ...                      data_name=0)\n",
        "        >>> iris3 = fetch_mldata('datasets-UCI iris',\n",
        "        ...                      target_name='class', data_name='double0')\n",
        "    \n",
        "    fetch_olivetti_faces(data_home=None, shuffle=False, random_state=0, download_if_missing=True)\n",
        "        Loader for the Olivetti faces data-set from AT&T.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        data_home : optional, default: None\n",
        "            Specify another download and cache folder for the datasets. By default\n",
        "            all scikit learn data is stored in '~/scikit_learn_data' subfolders.\n",
        "        \n",
        "        shuffle : boolean, optional\n",
        "            If True the order of the dataset is shuffled to avoid having\n",
        "            images of the same person grouped.\n",
        "        \n",
        "        download_if_missing: optional, True by default\n",
        "            If False, raise a IOError if the data is not locally available\n",
        "            instead of trying to download the data from the source site.\n",
        "        \n",
        "        random_state : optional, integer or RandomState object\n",
        "            The seed or the random number generator used to shuffle the\n",
        "            data.\n",
        "        \n",
        "        Notes\n",
        "        ------\n",
        "        \n",
        "        This dataset consists of 10 pictures each of 40 individuals. The original\n",
        "        database was available from (now defunct)\n",
        "        \n",
        "            http://www.uk.research.att.com/facedatabase.html\n",
        "        \n",
        "        The version retrieved here comes in MATLAB format from the personal\n",
        "        web page of Sam Roweis:\n",
        "        \n",
        "            http://www.cs.nyu.edu/~roweis/\n",
        "    \n",
        "    fetch_species_distributions(data_home=None, download_if_missing=True)\n",
        "        Loader for species distribution dataset from Phillips et. al. (2006)\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        data_home : optional, default: None\n",
        "            Specify another download and cache folder for the datasets. By default\n",
        "            all scikit learn data is stored in '~/scikit_learn_data' subfolders.\n",
        "        \n",
        "        download_if_missing: optional, True by default\n",
        "            If False, raise a IOError if the data is not locally available\n",
        "            instead of trying to download the data from the source site.\n",
        "        \n",
        "        Notes\n",
        "        ------\n",
        "        \n",
        "        This dataset represents the geographic distribution of species.\n",
        "        The dataset is provided by Phillips et. al. (2006).\n",
        "        \n",
        "        The two species are:\n",
        "        \n",
        "        - `\"Bradypus variegatus\"\n",
        "          <http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,\n",
        "          the Brown-throated Sloth.\n",
        "        \n",
        "        - `\"Microryzomys minutus\"\n",
        "          <http://www.iucnredlist.org/apps/redlist/details/13408/0>`_ ,\n",
        "          also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n",
        "          Colombia, Ecuador, Peru, and Venezuela.\n",
        "        \n",
        "        The data is returned as a Bunch object with the following attributes:\n",
        "        \n",
        "        coverages : array, shape = [14, 1592, 1212]\n",
        "            These represent the 14 features measured at each point of the map grid.\n",
        "            The latitude/longitude values for the grid are discussed below.\n",
        "            Missing data is represented by the value -9999.\n",
        "        \n",
        "        train : record array, shape = (1623,)\n",
        "            The training points for the data.  Each point has three fields:\n",
        "        \n",
        "            - train['species'] is the species name\n",
        "            - train['dd long'] is the longitude, in degrees\n",
        "            - train['dd lat'] is the latitude, in degrees\n",
        "        \n",
        "        test : record array, shape = (619,)\n",
        "            The test points for the data.  Same format as the training data.\n",
        "        \n",
        "        Nx, Ny : integers\n",
        "            The number of longitudes (x) and latitudes (y) in the grid\n",
        "        \n",
        "        x_left_lower_corner, y_left_lower_corner : floats\n",
        "            The (x,y) position of the lower-left corner, in degrees\n",
        "        \n",
        "        grid_size : float\n",
        "            The spacing between points of the grid, in degrees\n",
        "        \n",
        "        References\n",
        "        ----------\n",
        "        \n",
        "        * `\"Maximum entropy modeling of species geographic distributions\"\n",
        "          <http://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf>`_\n",
        "          S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n",
        "          190:231-259, 2006.\n",
        "        \n",
        "        Notes\n",
        "        -----\n",
        "        \n",
        "        * See examples/applications/plot_species_distribution_modeling.py\n",
        "          for an example of using this dataset with scikit-learn\n",
        "    \n",
        "    get_data_home(data_home=None)\n",
        "        Return the path of the scikit-learn data dir.\n",
        "        \n",
        "        This folder is used by some large dataset loaders to avoid\n",
        "        downloading the data several times.\n",
        "        \n",
        "        By default the data dir is set to a folder named 'scikit_learn_data'\n",
        "        in the user home folder.\n",
        "        \n",
        "        Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment\n",
        "        variable or programatically by giving an explit folder path. The\n",
        "        '~' symbol is expanded to the user home folder.\n",
        "        \n",
        "        If the folder does not already exist, it is automatically created.\n",
        "    \n",
        "    load_boston()\n",
        "        Load and return the boston house-prices dataset (regression).\n",
        "        \n",
        "        ==============     ==============\n",
        "        Samples total                 506\n",
        "        Dimensionality                 13\n",
        "        Features           real, positive\n",
        "        Targets             real 5. - 50.\n",
        "        ==============     ==============\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are:\n",
        "            'data', the data to learn, 'target', the regression targets,\n",
        "            'target_names', the meaning of the labels, and 'DESCR', the\n",
        "            full description of the dataset.\n",
        "        \n",
        "        Examples\n",
        "        --------\n",
        "        >>> from sklearn.datasets import load_boston\n",
        "        >>> boston = load_boston()\n",
        "        >>> print boston.data.shape\n",
        "        (506, 13)\n",
        "    \n",
        "    load_diabetes()\n",
        "        Load and return the diabetes dataset (regression).\n",
        "        \n",
        "        ==============      ==================\n",
        "        Samples total       442\n",
        "        Dimensionality      10\n",
        "        Features            real, -.2 < x < .2\n",
        "        Targets             integer 25 - 346\n",
        "        ==============      ==================\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are:\n",
        "            'data', the data to learn and 'target', the regression target for each\n",
        "            sample.\n",
        "    \n",
        "    load_digits(n_class=10)\n",
        "        Load and return the digits dataset (classification).\n",
        "        \n",
        "        Each datapoint is a 8x8 image of a digit.\n",
        "        \n",
        "        =================   ==============\n",
        "        Classes                         10\n",
        "        Samples per class             ~180\n",
        "        Samples total                 1797\n",
        "        Dimensionality                  64\n",
        "        Features             integers 0-16\n",
        "        =================   ==============\n",
        "        \n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_class : integer, between 0 and 10, optional (default=10)\n",
        "            The number of classes to return.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are:\n",
        "            'data', the data to learn, 'images', the images corresponding\n",
        "            to each sample, 'target', the classification labels for each\n",
        "            sample, 'target_names', the meaning of the labels, and 'DESCR',\n",
        "            the full description of the dataset.\n",
        "        \n",
        "        Examples\n",
        "        --------\n",
        "        To load the data and visualize the images::\n",
        "        \n",
        "            >>> from sklearn.datasets import load_digits\n",
        "            >>> digits = load_digits()\n",
        "            >>> print digits.data.shape\n",
        "            (1797, 64)\n",
        "            >>> import pylab as pl #doctest: +SKIP\n",
        "            >>> pl.gray() #doctest: +SKIP\n",
        "            >>> pl.matshow(digits.images[0]) #doctest: +SKIP\n",
        "            >>> pl.show() #doctest: +SKIP\n",
        "    \n",
        "    load_filenames(*args, **kwargs)\n",
        "        DEPRECATED: to be removed in 0.9; use sklearn.datasets.load_files instead\n",
        "        \n",
        "        Deprecated, use ``sklearn.datasets.load_files`` instead\n",
        "    \n",
        "    load_files(container_path, description=None, categories=None, load_content=True, shuffle=True, charset=None, charse_error='strict', random_state=0)\n",
        "        Load text files with categories as subfolder names.\n",
        "        \n",
        "        Individual samples are assumed to be files stored a two levels folder\n",
        "        structure such as the following:\n",
        "        \n",
        "            container_folder/\n",
        "                category_1_folder/\n",
        "                    file_1.txt\n",
        "                    file_2.txt\n",
        "                    ...\n",
        "                    file_42.txt\n",
        "                category_2_folder/\n",
        "                    file_43.txt\n",
        "                    file_44.txt\n",
        "                    ...\n",
        "        \n",
        "        The folder names are used has supervised signal label names. The indivial\n",
        "        file names are not important.\n",
        "        \n",
        "        This function does not try to extract features into a numpy array or\n",
        "        scipy sparse matrix. In addition, if load_content is false it\n",
        "        does not try to load the files in memory.\n",
        "        \n",
        "        To use utf-8 text files in a scikit-learn classification or clustering\n",
        "        algorithm you will first need to use the `sklearn.features.text`\n",
        "        module to build a feature extraction transformer that suits your\n",
        "        problem.\n",
        "        \n",
        "        Similar feature extractors should be build for other kind of unstructured\n",
        "        data input such as images, audio, video, ...\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        container_path : string or unicode\n",
        "            Path to the main folder holding one subfolder per category\n",
        "        \n",
        "        description: string or unicode, optional (default=None)\n",
        "            A paragraph describing the characteristic of the dataset: its source,\n",
        "            reference, etc.\n",
        "        \n",
        "        categories : A collection of strings or None, optional (default=None)\n",
        "            If None (default), load all the categories.\n",
        "            If not None, list of category names to load (other categories ignored).\n",
        "        \n",
        "        load_content : boolean, optional (default=True)\n",
        "            Whether to load or not the content of the different files. If\n",
        "            true a 'data' attribute containing the text information is present\n",
        "            in the data structure returned. If not, a filenames attribute\n",
        "            gives the path to the files.\n",
        "        \n",
        "        charset : string or None (default is None)\n",
        "            If None, do not try to decode the content of the files (e.g. for\n",
        "            images or other non-text content).\n",
        "            If not None, charset to use to decode text files if load_content is\n",
        "            True.\n",
        "        \n",
        "        charset_error: {'strict', 'ignore', 'replace'}\n",
        "            Instruction on what to do if a byte sequence is given to analyze that\n",
        "            contains characters not of the given `charset`. By default, it is\n",
        "            'strict', meaning that a UnicodeDecodeError will be raised. Other\n",
        "            values are 'ignore' and 'replace'.\n",
        "        \n",
        "        shuffle : bool, optional (default=True)\n",
        "            Whether or not to shuffle the data: might be important for models that\n",
        "            make the assumption that the samples are independent and identically\n",
        "            distributed (i.i.d.), such as stochastic gradient descent.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=0)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are: either\n",
        "            data, the raw text data to learn, or 'filenames', the files\n",
        "            holding it, 'target', the classification labels (integer index),\n",
        "            'target_names', the meaning of the labels, and 'DESCR', the full\n",
        "            description of the dataset.\n",
        "    \n",
        "    load_iris()\n",
        "        Load and return the iris dataset (classification).\n",
        "        \n",
        "        The iris dataset is a classic and very easy multi-class classification\n",
        "        dataset.\n",
        "        \n",
        "        =================   ==============\n",
        "        Classes                          3\n",
        "        Samples per class               50\n",
        "        Samples total                  150\n",
        "        Dimensionality                   4\n",
        "        Features            real, positive\n",
        "        =================   ==============\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are:\n",
        "            'data', the data to learn, 'target', the classification labels,\n",
        "            'target_names', the meaning of the labels, 'feature_names', the\n",
        "            meaning of the features, and 'DESCR', the\n",
        "            full description of the dataset.\n",
        "        \n",
        "        Examples\n",
        "        --------\n",
        "        Let's say you are interested in the samples 10, 25, and 50, and want to\n",
        "        know their class name.\n",
        "        \n",
        "        >>> from sklearn.datasets import load_iris\n",
        "        >>> data = load_iris()\n",
        "        >>> data.target[[10, 25, 50]]\n",
        "        array([0, 0, 1])\n",
        "        >>> list(data.target_names)\n",
        "        ['setosa', 'versicolor', 'virginica']\n",
        "    \n",
        "    load_lfw_pairs(download_if_missing=False, **kwargs)\n",
        "        Alias for fetch_lfw_pairs(download_if_missing=False)\n",
        "        \n",
        "        Check fetch_lfw_pairs.__doc__ for the documentation and parameter list.\n",
        "    \n",
        "    load_lfw_people(download_if_missing=False, **kwargs)\n",
        "        Alias for fetch_lfw_people(download_if_missing=False)\n",
        "        \n",
        "        Check fetch_lfw_people.__doc__ for the documentation and parameter list.\n",
        "    \n",
        "    load_linnerud()\n",
        "        Load and return the linnerud dataset (multivariate regression).\n",
        "        \n",
        "        Samples total: 20\n",
        "        Dimensionality: 3 for both data and targets\n",
        "        Features: integer\n",
        "        Targets: integer\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are: 'data' and\n",
        "            'targets', the two multivariate datasets, with 'data' corresponding to\n",
        "            the exercise and 'targets' corresponding to the physiological\n",
        "            measurements, as well as 'feature_names' and 'target_names'.\n",
        "    \n",
        "    load_mlcomp(name_or_id, set_='raw', mlcomp_root=None, **kwargs)\n",
        "        Load a datasets as downloaded from http://mlcomp.org\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        \n",
        "        name_or_id : the integer id or the string name metadata of the MLComp\n",
        "                     dataset to load\n",
        "        \n",
        "        set_ : select the portion to load: 'train', 'test' or 'raw'\n",
        "        \n",
        "        mlcomp_root : the filesystem path to the root folder where MLComp datasets\n",
        "                      are stored, if mlcomp_root is None, the MLCOMP_DATASETS_HOME\n",
        "                      environment variable is looked up instead.\n",
        "        \n",
        "        **kwargs : domain specific kwargs to be passed to the dataset loader.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        \n",
        "        data : Bunch\n",
        "            Dictionary-like object, the interesting attributes are:\n",
        "            'filenames', the files holding the raw to learn, 'target', the\n",
        "            classification labels (integer index), 'target_names',\n",
        "            the meaning of the labels, and 'DESCR', the full description of the\n",
        "            dataset.\n",
        "        \n",
        "        Note on the lookup process: depending on the type of name_or_id,\n",
        "        will choose between integer id lookup or metadata name lookup by\n",
        "        looking at the unzipped archives and metadata file.\n",
        "        \n",
        "        TODO: implement zip dataset loading too\n",
        "    \n",
        "    load_sample_image(image_name)\n",
        "        Load the numpy array of a single sample image\n",
        "        \n",
        "        Parameters\n",
        "        -----------\n",
        "        image_name: {`china.jpg`, `flower.jpg`}\n",
        "            The name of the sample image loaded\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        img: 3D array\n",
        "            The image as a numpy array: height x width x color\n",
        "        \n",
        "        Examples\n",
        "        ---------\n",
        "        \n",
        "        >>> from sklearn.datasets import load_sample_image\n",
        "        >>> china = load_sample_image('china.jpg')   # doctest: +SKIP\n",
        "        >>> china.dtype                              # doctest: +SKIP\n",
        "        dtype('uint8')\n",
        "        >>> china.shape                              # doctest: +SKIP\n",
        "        (427, 640, 3)\n",
        "        >>> flower = load_sample_image('flower.jpg') # doctest: +SKIP\n",
        "        >>> flower.dtype                             # doctest: +SKIP\n",
        "        dtype('uint8')\n",
        "        >>> flower.shape                             # doctest: +SKIP\n",
        "        (427, 640, 3)\n",
        "    \n",
        "    load_sample_images()\n",
        "        Load sample images for image manipulation.\n",
        "        Loads both, ``china`` and ``flower``.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data : Bunch\n",
        "            Dictionary-like object with the following attributes :\n",
        "            'images', the two sample images, 'filenames', the file\n",
        "            names for the images, and 'DESCR'\n",
        "            the full description of the dataset.\n",
        "        \n",
        "        Examples\n",
        "        --------\n",
        "        To load the data and visualize the images:\n",
        "        \n",
        "        >>> from sklearn.datasets import load_sample_images\n",
        "        >>> dataset = load_sample_images()     #doctest: +SKIP\n",
        "        >>> len(dataset.images)                #doctest: +SKIP\n",
        "        2\n",
        "        >>> first_img_data = dataset.images[0] #doctest: +SKIP\n",
        "        >>> first_img_data.shape               #doctest: +SKIP\n",
        "        (427, 640, 3)\n",
        "        >>> first_img_data.dtype               #doctest: +SKIP\n",
        "        dtype('uint8')\n",
        "    \n",
        "    load_svmlight_file(f, n_features=None, dtype=<type 'numpy.float64'>, multilabel=False, zero_based='auto', query_id=False)\n",
        "        Load datasets in the svmlight / libsvm format into sparse CSR matrix\n",
        "        \n",
        "        This format is a text-based format, with one sample per line. It does\n",
        "        not store zero valued features hence is suitable for sparse dataset.\n",
        "        \n",
        "        The first element of each line can be used to store a target variable\n",
        "        to predict.\n",
        "        \n",
        "        This format is used as the default format for both svmlight and the\n",
        "        libsvm command line programs.\n",
        "        \n",
        "        Parsing a text based source can be expensive. When working on\n",
        "        repeatedly on the same dataset, it is recommended to wrap this\n",
        "        loader with joblib.Memory.cache to store a memmapped backup of the\n",
        "        CSR results of the first call and benefit from the near instantaneous\n",
        "        loading of memmapped structures for the subsequent calls.\n",
        "        \n",
        "        This implementation is naive: it does allocate too much memory and\n",
        "        is slow since written in python. On large datasets it is recommended\n",
        "        to use an optimized loader such as:\n",
        "        \n",
        "          https://github.com/mblondel/svmlight-loader\n",
        "        \n",
        "        In case the file contains a pairwise preference constraint (known\n",
        "        as \"qid\" in the svmlight format) these are ignored unless the\n",
        "        query_id parameter is set to True. These pairwise preference\n",
        "        constraints can be used to contraint the combination of samples\n",
        "        when using pairwise loss functions (as is the case in some\n",
        "        learning to rank problems) so that only pairs with the same\n",
        "        query_id value are considered.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        f: {str, file-like, int}\n",
        "            (Path to) a file to load. If a path ends in \".gz\" or \".bz2\", it will\n",
        "            be uncompressed on the fly. If an integer is passed, it is assumed to\n",
        "            be a file descriptor. A file-like or file descriptor will not be closed\n",
        "            by this function. A file-like object must be opened in binary mode.\n",
        "        \n",
        "        n_features: int or None\n",
        "            The number of features to use. If None, it will be inferred. This\n",
        "            argument is useful to load several files that are subsets of a\n",
        "            bigger sliced dataset: each subset might not have example of\n",
        "            every feature, hence the inferred shape might vary from one\n",
        "            slice to another.\n",
        "        \n",
        "        multilabel: boolean, optional\n",
        "            Samples may have several labels each (see\n",
        "            http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)\n",
        "        \n",
        "        zero_based: boolean or \"auto\", optional\n",
        "            Whether column indices in f are zero-based (True) or one-based\n",
        "            (False). If column indices are one-based, they are transformed to\n",
        "            zero-based to match Python/NumPy conventions.\n",
        "            If set to \"auto\", a heuristic check is applied to determine this from\n",
        "            the file contents. Both kinds of files occur \"in the wild\", but they\n",
        "            are unfortunately not self-identifying. Using \"auto\" or True should\n",
        "            always be safe.\n",
        "        \n",
        "        query_id: boolean, defaults to False\n",
        "            If True, will return the query_id array for each file.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X: scipy.sparse matrix of shape (n_samples, n_features)\n",
        "        \n",
        "        y: ndarray of shape (n_samples,), or, in the multilabel a list of\n",
        "            tuples of length n_samples.\n",
        "        \n",
        "        query_id: array of shape (n_samples,)\n",
        "           query_id for each sample. Only returned when query_id is set to\n",
        "           True.\n",
        "        \n",
        "        See also\n",
        "        --------\n",
        "        load_svmlight_files: similar function for loading multiple files in this\n",
        "        format, enforcing the same number of features/columns on all of them.\n",
        "    \n",
        "    load_svmlight_files(files, n_features=None, dtype=<type 'numpy.float64'>, multilabel=False, zero_based='auto', query_id=False)\n",
        "        Load dataset from multiple files in SVMlight format\n",
        "        \n",
        "        This function is equivalent to mapping load_svmlight_file over a list of\n",
        "        files, except that the results are concatenated into a single, flat list\n",
        "        and the samples vectors are constrained to all have the same number of\n",
        "        features.\n",
        "        \n",
        "        In case the file contains a pairwise preference constraint (known\n",
        "        as \"qid\" in the svmlight format) these are ignored unless the\n",
        "        query_id parameter is set to True. These pairwise preference\n",
        "        constraints can be used to constraint the combination of samples\n",
        "        when using pairwise loss functions (as is the case in some\n",
        "        learning to rank problems) so that only pairs with the same\n",
        "        query_id value are considered.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        files : iterable over {str, file-like, int}\n",
        "            (Paths of) files to load. If a path ends in \".gz\" or \".bz2\", it will\n",
        "            be uncompressed on the fly. If an integer is passed, it is assumed to\n",
        "            be a file descriptor. File-likes and file descriptors will not be\n",
        "            closed by this function. File-like objects must be opened in binary\n",
        "            mode.\n",
        "        \n",
        "        n_features: int or None\n",
        "            The number of features to use. If None, it will be inferred from the\n",
        "            maximum column index occurring in any of the files.\n",
        "        \n",
        "        multilabel: boolean, optional\n",
        "            Samples may have several labels each (see\n",
        "            http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)\n",
        "        \n",
        "        zero_based: boolean or \"auto\", optional\n",
        "            Whether column indices in f are zero-based (True) or one-based\n",
        "            (False). If column indices are one-based, they are transformed to\n",
        "            zero-based to match Python/NumPy conventions.\n",
        "            If set to \"auto\", a heuristic check is applied to determine this from\n",
        "            the file contents. Both kinds of files occur \"in the wild\", but they\n",
        "            are unfortunately not self-identifying. Using \"auto\" or True should\n",
        "            always be safe.\n",
        "        \n",
        "        query_id: boolean, defaults to False\n",
        "            If True, will return the query_id array for each file.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        [X1, y1, ..., Xn, yn]\n",
        "        where each (Xi, yi) pair is the result from load_svmlight_file(files[i]).\n",
        "        \n",
        "        If query_id is set to True, this will return instead [X1, y1, q1,\n",
        "        ..., Xn, yn, qn] where (Xi, yi, qi) is the result from\n",
        "        load_svmlight_file(files[i])\n",
        "        \n",
        "        Rationale\n",
        "        ---------\n",
        "        When fitting a model to a matrix X_train and evaluating it against a\n",
        "        matrix X_test, it is essential that X_train and X_test have the same\n",
        "        number of features (X_train.shape[1] == X_test.shape[1]). This may not\n",
        "        be the case if you load the files individually with load_svmlight_file.\n",
        "        \n",
        "        See also\n",
        "        --------\n",
        "        load_svmlight_file\n",
        "    \n",
        "    make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)\n",
        "        Generate isotropic Gaussian blobs for clustering.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The total number of points equally divided among clusters.\n",
        "        \n",
        "        n_features : int, optional (default=2)\n",
        "            The number of features for each sample.\n",
        "        \n",
        "        centers : int or array of shape [n_centers, n_features], optional\n",
        "            (default=3)\n",
        "            The number of centers to generate, or the fixed center locations.\n",
        "        \n",
        "        cluster_std: float or sequence of floats, optional (default=1.0)\n",
        "            The standard deviation of the clusters.\n",
        "        \n",
        "        center_box: pair of floats (min, max), optional (default=(-10.0, 10.0))\n",
        "            The bounding box for each cluster center when centers are\n",
        "            generated at random.\n",
        "        \n",
        "        shuffle : boolean, optional (default=True)\n",
        "            Shuffle the samples.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The generated samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The integer labels for cluster membership of each sample.\n",
        "        \n",
        "        Examples\n",
        "        --------\n",
        "        >>> from sklearn.datasets.samples_generator import make_blobs\n",
        "        >>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,\n",
        "        ...                   random_state=0)\n",
        "        >>> print X.shape\n",
        "        (10, 2)\n",
        "        >>> y\n",
        "        array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])\n",
        "    \n",
        "    make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8)\n",
        "        Make a large circle containing a smaller circle in 2d.\n",
        "        \n",
        "        A simple toy dataset to visualize clustering and classification\n",
        "        algorithms.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The total number of points generated.\n",
        "        \n",
        "        shuffle: bool, optional (default=True)\n",
        "            Whether to shuffle the samples.\n",
        "        \n",
        "        noise : double or None (default=None)\n",
        "            Standard deviation of Gaussian noise added to the data.\n",
        "        \n",
        "        factor : double < 1 (default=.8)\n",
        "            Scale factor between inner and outer circle.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, 2]\n",
        "            The generated samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The integer labels (0 or 1) for class membership of each sample.\n",
        "    \n",
        "    make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None)\n",
        "        Generate a random n-class classification problem.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        n_features : int, optional (default=20)\n",
        "            The total number of features. These comprise `n_informative`\n",
        "            informative features, `n_redundant` redundant features, `n_repeated`\n",
        "            dupplicated features and `n_features-n_informative-n_redundant-\n",
        "            n_repeated` useless features drawn at random.\n",
        "        \n",
        "        n_informative : int, optional (default=2)\n",
        "            The number of informative features. Each class is composed of a number\n",
        "            of gaussian clusters each located around the vertices of a hypercube\n",
        "            in a subspace of dimension `n_informative`. For each cluster,\n",
        "            informative features are drawn independently from  N(0, 1) and then\n",
        "            randomly linearly combined in order to add covariance. The clusters\n",
        "            are then placed on the vertices of the hypercube.\n",
        "        \n",
        "        n_redundant : int, optional (default=2)\n",
        "            The number of redundant features. These features are generated as\n",
        "            random linear combinations of the informative features.\n",
        "        \n",
        "        n_repeated : int, optional (default=2)\n",
        "            The number of dupplicated features, drawn randomly from the informative\n",
        "            and the redundant features.\n",
        "        \n",
        "        n_classes : int, optional (default=2)\n",
        "            The number of classes (or labels) of the classification problem.\n",
        "        \n",
        "        n_clusters_per_class : int, optional (default=2)\n",
        "            The number of clusters per class.\n",
        "        \n",
        "        weights : list of floats or None (default=None)\n",
        "            The proportions of samples assigned to each class. If None, then\n",
        "            classes are balanced. Note that if `len(weights) == n_classes - 1`,\n",
        "            then the last class weight is automatically inferred.\n",
        "        \n",
        "        flip_y : float, optional (default=0.01)\n",
        "            The fraction of samples whose class are randomly exchanged.\n",
        "        \n",
        "        class_sep : float, optional (default=1.0)\n",
        "            The factor multiplying the hypercube dimension.\n",
        "        \n",
        "        hypercube : boolean, optional (default=True)\n",
        "            If True, the clusters are put on the vertices of a hypercube. If\n",
        "            False, the clusters are put on the vertices of a random polytope.\n",
        "        \n",
        "        shift : float or None, optional (default=0.0)\n",
        "            Shift all features by the specified value. If None, then features\n",
        "            are shifted by a random value drawn in [-class_sep, class_sep].\n",
        "        \n",
        "        scale : float or None, optional (default=1.0)\n",
        "            Multiply all features by the specified value. If None, then features\n",
        "            are scaled by a random value drawn in [1, 100]. Note that scaling\n",
        "            happens after shifting.\n",
        "        \n",
        "        shuffle : boolean, optional (default=True)\n",
        "            Shuffle the samples and the features.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The generated samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The integer labels for class membership of each sample.\n",
        "        \n",
        "        Notes\n",
        "        -----\n",
        "        The algorithm is adapted from Guyon [1] and was designed to generate\n",
        "        the \"Madelon\" dataset.\n",
        "        \n",
        "        References\n",
        "        ----------\n",
        "        \n",
        "        .. [1] I. Guyon, \"Design of experiments for the NIPS 2003 variable\n",
        "               selection benchmark\", 2003.\n",
        "    \n",
        "    make_friedman1(n_samples=100, n_features=10, noise=0.0, random_state=None)\n",
        "        Generate the \"Friedman #1\" regression problem\n",
        "        \n",
        "        This dataset is described in Friedman [1] and Breiman [2].\n",
        "        \n",
        "        Inputs `X` are independent features uniformly distributed on the interval\n",
        "        [0, 1]. The output `y` is created according to the formula::\n",
        "        \n",
        "            y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 + 10 * X[:, 3] + 5 * X[:, 4] + noise * N(0, 1).\n",
        "        \n",
        "        Out of the `n_features` features, only 5 are actually used to compute\n",
        "        `y`. The remaining features are independent of `y`.\n",
        "        \n",
        "        The number of features has to be >= 5.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        n_features : int, optional (default=10)\n",
        "            The number of features. Should be at least 5.\n",
        "        \n",
        "        noise : float, optional (default=0.0)\n",
        "            The standard deviation of the gaussian noise applied to the output.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The input samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The output values.\n",
        "        \n",
        "        References\n",
        "        ----------\n",
        "        \n",
        "        .. [1] J. Friedman, \"Multivariate adaptive regression splines\", The Annals\n",
        "               of Statistics 19 (1), pages 1-67, 1991.\n",
        "        \n",
        "        .. [2] L. Breiman, \"Bagging predictors\", Machine Learning 24,\n",
        "               pages 123-140, 1996.\n",
        "    \n",
        "    make_friedman2(n_samples=100, noise=0.0, random_state=None)\n",
        "        Generate the \"Friedman #2\" regression problem\n",
        "        \n",
        "        This dataset is described in Friedman [1] and Breiman [2].\n",
        "        \n",
        "        Inputs `X` are 4 independent features uniformly distributed on the\n",
        "        intervals::\n",
        "        \n",
        "            0 <= X[:, 0] <= 100,\n",
        "            40 * pi <= X[:, 1] <= 560 * pi,\n",
        "            0 <= X[:, 2] <= 1,\n",
        "            1 <= X[:, 3] <= 11.\n",
        "        \n",
        "        The output `y` is created according to the formula::\n",
        "        \n",
        "            y(X) = (X[:, 0] ** 2 + (X[:, 1] * X[:, 2]  - 1 / (X[:, 1] * X[:, 3])) ** 2) ** 0.5 + noise * N(0, 1).\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        noise : float, optional (default=0.0)\n",
        "            The standard deviation of the gaussian noise applied to the output.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, 4]\n",
        "            The input samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The output values.\n",
        "        \n",
        "        References\n",
        "        ----------\n",
        "        \n",
        "        .. [1] J. Friedman, \"Multivariate adaptive regression splines\", The Annals\n",
        "               of Statistics 19 (1), pages 1-67, 1991.\n",
        "        \n",
        "        .. [2] L. Breiman, \"Bagging predictors\", Machine Learning 24,\n",
        "               pages 123-140, 1996.\n",
        "    \n",
        "    make_friedman3(n_samples=100, noise=0.0, random_state=None)\n",
        "        Generate the \"Friedman #3\" regression problem\n",
        "        \n",
        "        This dataset is described in Friedman [1] and Breiman [2].\n",
        "        \n",
        "        Inputs `X` are 4 independent features uniformly distributed on the\n",
        "        intervals::\n",
        "        \n",
        "            0 <= X[:, 0] <= 100,\n",
        "            40 * pi <= X[:, 1] <= 560 * pi,\n",
        "            0 <= X[:, 2] <= 1,\n",
        "            1 <= X[:, 3] <= 11.\n",
        "        \n",
        "        The output `y` is created according to the formula::\n",
        "        \n",
        "            y(X) = arctan((X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) / X[:, 0]) + noise * N(0, 1).\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        noise : float, optional (default=0.0)\n",
        "            The standard deviation of the gaussian noise applied to the output.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, 4]\n",
        "            The input samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The output values.\n",
        "        \n",
        "        References\n",
        "        ----------\n",
        "        \n",
        "        .. [1] J. Friedman, \"Multivariate adaptive regression splines\", The Annals\n",
        "               of Statistics 19 (1), pages 1-67, 1991.\n",
        "        \n",
        "        .. [2] L. Breiman, \"Bagging predictors\", Machine Learning 24,\n",
        "               pages 123-140, 1996.\n",
        "    \n",
        "    make_hastie_10_2(n_samples=12000, random_state=None)\n",
        "        Generates data for binary classification used in\n",
        "        Hastie et al. 2009, Example 10.2.\n",
        "        \n",
        "        The ten features are standard independent Gaussian and\n",
        "        the target ``y`` is defined by::\n",
        "        \n",
        "          y[i] = 1 if np.sum(X[i] > 9.34 else -1\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=12000)\n",
        "            The number of samples.\n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, 10]\n",
        "            The input samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The output values.\n",
        "        \n",
        "        **References**:\n",
        "        \n",
        "        .. [1] T. Hastie, R. Tibshirani and J. Friedman, \"Elements of Statistical\n",
        "        Learning Ed. 2\", Springer, 2009.\n",
        "    \n",
        "    make_low_rank_matrix(n_samples=100, n_features=100, effective_rank=10, tail_strength=0.5, random_state=None)\n",
        "        Generate a mostly low rank matrix with bell-shaped singular values\n",
        "        \n",
        "        Most of the variance can be explained by a bell-shaped curve of width\n",
        "        effective_rank: the low rank part of the singular values profile is::\n",
        "        \n",
        "            (1 - tail_strength) * exp(-1.0 * (i / effective_rank) ** 2)\n",
        "        \n",
        "        The remaining singular values' tail is fat, decreasing as::\n",
        "        \n",
        "            tail_strength * exp(-0.1 * i / effective_rank).\n",
        "        \n",
        "        The low rank part of the profile can be considered the structured\n",
        "        signal part of the data while the tail can be considered the noisy\n",
        "        part of the data that cannot be summarized by a low number of linear\n",
        "        components (singular vectors).\n",
        "        \n",
        "        This kind of singular profiles is often seen in practice, for instance:\n",
        "         - gray level pictures of faces\n",
        "         - TF-IDF vectors of text documents crawled from the web\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        n_features : int, optional (default=100)\n",
        "            The number of features.\n",
        "        \n",
        "        effective_rank : int, optional (default=10)\n",
        "            The approximate number of singular vectors required to explain most of\n",
        "            the data by linear combinations.\n",
        "        \n",
        "        tail_strength : float between 0.0 and 1.0, optional (default=0.5)\n",
        "            The relative importance of the fat noisy tail of the singular values\n",
        "            profile.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The matrix.\n",
        "    \n",
        "    make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)\n",
        "        Make two interleaving half circles\n",
        "        \n",
        "        A simple toy dataset to visualize clustering and classification\n",
        "        algorithms.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The total number of points generated.\n",
        "        \n",
        "        shuffle : bool, optional (default=True)\n",
        "            Whether to shuffle the samples.\n",
        "        \n",
        "        noise : double or None (default=None)\n",
        "            Standard deviation of Gaussian noise added to the data.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, 2]\n",
        "            The generated samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The integer labels (0 or 1) for class membership of each sample.\n",
        "    \n",
        "    make_multilabel_classification(n_samples=100, n_features=20, n_classes=5, n_labels=2, length=50, allow_unlabeled=True, random_state=None)\n",
        "        Generate a random multilabel classification problem.\n",
        "        \n",
        "        For each sample, the generative process is:\n",
        "            - pick the number of labels: n ~ Poisson(n_labels)\n",
        "            - n times, choose a class c: c ~ Multinomial(theta)\n",
        "            - pick the document length: k ~ Poisson(length)\n",
        "            - k times, choose a word: w ~ Multinomial(theta_c)\n",
        "        \n",
        "        In the above process, rejection sampling is used to make sure that\n",
        "        n is never zero or more than `n_classes`, and that the document length\n",
        "        is never zero. Likewise, we reject classes which have already been chosen.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        n_features : int, optional (default=20)\n",
        "            The total number of features.\n",
        "        \n",
        "        n_classes : int, optional (default=5)\n",
        "            The number of classes of the classification problem.\n",
        "        \n",
        "        n_labels : int, optional (default=2)\n",
        "            The average number of labels per instance. Number of labels follows\n",
        "            a Poisson distribution that never takes the value 0.\n",
        "        \n",
        "        length : int, optional (default=50)\n",
        "            Sum of the features (number of words if documents).\n",
        "        \n",
        "        allow_unlabeled : bool, optional (default=True)\n",
        "            If ``True``, some instances might not belong to any class.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The generated samples.\n",
        "        \n",
        "        Y : list of tuples\n",
        "            The label sets.\n",
        "    \n",
        "    make_regression(n_samples=100, n_features=100, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0, shuffle=True, coef=False, random_state=None)\n",
        "        Generate a random regression problem.\n",
        "        \n",
        "        The input set can either be well conditioned (by default) or have a low\n",
        "        rank-fat tail singular profile. See the `make_low_rank_matrix` for\n",
        "        more details.\n",
        "        \n",
        "        The output is generated by applying a (potentially biased) random linear\n",
        "        regression model with `n_informative` nonzero regressors to the previously\n",
        "        generated input and some gaussian centered noise with some adjustable\n",
        "        scale.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        n_features : int, optional (default=100)\n",
        "            The number of features.\n",
        "        \n",
        "        n_informative : int, optional (default=10)\n",
        "            The number of informative features, i.e., the number of features used\n",
        "            to build the linear model used to generate the output.\n",
        "        \n",
        "        n_targets : int, optional (default=1)\n",
        "            The number of regression targets, i.e., the dimension of the y output\n",
        "            vector associated with a sample. By default, the output is a scalar.\n",
        "        \n",
        "        bias : float, optional (default=0.0)\n",
        "            The bias term in the underlying linear model.\n",
        "        \n",
        "        effective_rank : int or None, optional (default=None)\n",
        "            if not None:\n",
        "                The approximate number of singular vectors required to explain most\n",
        "                of the input data by linear combinations. Using this kind of\n",
        "                singular spectrum in the input allows the generator to reproduce\n",
        "                the correlations often observed in practice.\n",
        "            if None:\n",
        "                The input set is well conditioned, centered and gaussian with\n",
        "                unit variance.\n",
        "        \n",
        "        tail_strength : float between 0.0 and 1.0, optional (default=0.5)\n",
        "            The relative importance of the fat noisy tail of the singular values\n",
        "            profile if `effective_rank` is not None.\n",
        "        \n",
        "        noise : float, optional (default=0.0)\n",
        "            The standard deviation of the gaussian noise applied to the output.\n",
        "        \n",
        "        shuffle : boolean, optional (default=True)\n",
        "            Shuffle the samples and the features.\n",
        "        \n",
        "        coef : boolean, optional (default=False)\n",
        "            If True, the coefficients of the underlying linear model are returned.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The input samples.\n",
        "        \n",
        "        y : array of shape [n_samples] or [n_samples, n_targets]\n",
        "            The output values.\n",
        "        \n",
        "        coef : array of shape [n_features] or [n_features, n_targets], optional\n",
        "            The coefficient of the underlying linear model. It is returned only if\n",
        "            coef is True.\n",
        "    \n",
        "    make_s_curve(n_samples=100, noise=0.0, random_state=None)\n",
        "        Generate an S curve dataset.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of sample points on the S curve.\n",
        "        \n",
        "        noise : float, optional (default=0.0)\n",
        "            The standard deviation of the gaussian noise.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, 3]\n",
        "            The points.\n",
        "        \n",
        "        t : array of shape [n_samples]\n",
        "            The univariate position of the sample according to the main dimension\n",
        "            of the points in the manifold.\n",
        "    \n",
        "    make_sparse_coded_signal(n_samples, n_components, n_features, n_nonzero_coefs, random_state=None)\n",
        "        Generate a signal as a sparse combination of dictionary elements.\n",
        "        \n",
        "        Returns a matrix Y = DX, such as D is (n_features, n_components),\n",
        "        X is (n_components, n_samples) and each column of X has exactly\n",
        "        n_nonzero_coefs non-zero elements.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int\n",
        "            number of samples to generate\n",
        "        \n",
        "        n_components:  int,\n",
        "            number of components in the dictionary\n",
        "        \n",
        "        n_features : int\n",
        "            number of features of the dataset to generate\n",
        "        \n",
        "        n_nonzero_coefs : int\n",
        "            number of active (non-zero) coefficients in each sample\n",
        "        \n",
        "        random_state: int or RandomState instance, optional (default=None)\n",
        "            seed used by the pseudo random number generator\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        data: array of shape [n_features, n_samples]\n",
        "            The encoded signal (Y).\n",
        "        \n",
        "        dictionary: array of shape [n_features, n_components]\n",
        "            The dictionary with normalized components (D).\n",
        "        \n",
        "        code: array of shape [n_components, n_samples]\n",
        "            The sparse code such that each column of this matrix has exactly\n",
        "            n_nonzero_coefs non-zero items (X).\n",
        "    \n",
        "    make_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, random_state=None)\n",
        "        Generate a sparse symetric definite positive matrix.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        dim: integer, optional (default=1)\n",
        "            The size of the random  (matrix to generate.\n",
        "        \n",
        "        alpha: float between 0 and 1, optional (default=0.95)\n",
        "            The probability that a coefficient is non zero (see notes).\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        prec: array of shape = [dim, dim]\n",
        "        \n",
        "        Notes\n",
        "        -----\n",
        "        The sparsity is actually imposed on the cholesky factor of the matrix.\n",
        "        Thus alpha does not translate directly into the filling fraction of\n",
        "        the matrix itself.\n",
        "    \n",
        "    make_sparse_uncorrelated(n_samples=100, n_features=10, random_state=None)\n",
        "        Generate a random regression problem with sparse uncorrelated design\n",
        "        \n",
        "        This dataset is described in Celeux et al [1]. as::\n",
        "        \n",
        "            X ~ N(0, 1)\n",
        "            y(X) = X[:, 0] + 2 * X[:, 1] - 2 * X[:, 2] - 1.5 * X[:, 3]\n",
        "        \n",
        "        Only the first 4 features are informative. The remaining features are\n",
        "        useless.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of samples.\n",
        "        \n",
        "        n_features : int, optional (default=10)\n",
        "            The number of features.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The input samples.\n",
        "        \n",
        "        y : array of shape [n_samples]\n",
        "            The output values.\n",
        "        \n",
        "        References\n",
        "        ----------\n",
        "        \n",
        "        .. [1] G. Celeux, M. El Anbari, J.-M. Marin, C. P. Robert,\n",
        "               \"Regularization in regression: comparing Bayesian and frequentist\n",
        "               methods in a poorly informative situation\", 2009.\n",
        "    \n",
        "    make_spd_matrix(n_dim, random_state=None)\n",
        "        Generate a random symmetric, positive-definite matrix.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_dim : int\n",
        "            The matrix dimension.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_dim, n_dim]\n",
        "            The random symmetric, positive-definite matrix.\n",
        "    \n",
        "    make_swiss_roll(n_samples=100, noise=0.0, random_state=None)\n",
        "        Generate a swiss roll dataset.\n",
        "        \n",
        "        Parameters\n",
        "        ----------\n",
        "        n_samples : int, optional (default=100)\n",
        "            The number of sample points on the S curve.\n",
        "        \n",
        "        noise : float, optional (default=0.0)\n",
        "            The standard deviation of the gaussian noise.\n",
        "        \n",
        "        random_state : int, RandomState instance or None, optional (default=None)\n",
        "            If int, random_state is the seed used by the random number generator;\n",
        "            If RandomState instance, random_state is the random number generator;\n",
        "            If None, the random number generator is the RandomState instance used\n",
        "            by `np.random`.\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        X : array of shape [n_samples, 3]\n",
        "            The points.\n",
        "        \n",
        "        t : array of shape [n_samples]\n",
        "            The univariate position of the sample according to the main dimension\n",
        "            of the points in the manifold.\n",
        "        \n",
        "        Notes\n",
        "        -----\n",
        "        The algorithm is from Marsland [1].\n",
        "        \n",
        "        References\n",
        "        ----------\n",
        "        \n",
        "        .. [1] S. Marsland, \"Machine Learning: An Algorithmic Perpsective\",\n",
        "               Chapter 10, 2009.\n",
        "               http://www-ist.massey.ac.nz/smarsland/Code/10/lle.py\n",
        "    \n",
        "    mldata_filename(dataname)\n",
        "        Convert a raw name for a data set in a mldata.org filename.\n",
        "\n",
        "DATA\n",
        "    __all__ = ['clear_data_home', 'dump_svmlight_file', 'fetch_20newsgroup...\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_iris\n",
      "iris = load_iris()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Inspect Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print iris.data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 5.1  3.5  1.4  0.2]\n",
        " [ 4.9  3.   1.4  0.2]\n",
        " [ 4.7  3.2  1.3  0.2]\n",
        " [ 4.6  3.1  1.5  0.2]\n",
        " [ 5.   3.6  1.4  0.2]\n",
        " [ 5.4  3.9  1.7  0.4]\n",
        " [ 4.6  3.4  1.4  0.3]\n",
        " [ 5.   3.4  1.5  0.2]\n",
        " [ 4.4  2.9  1.4  0.2]\n",
        " [ 4.9  3.1  1.5  0.1]\n",
        " [ 5.4  3.7  1.5  0.2]\n",
        " [ 4.8  3.4  1.6  0.2]\n",
        " [ 4.8  3.   1.4  0.1]\n",
        " [ 4.3  3.   1.1  0.1]\n",
        " [ 5.8  4.   1.2  0.2]\n",
        " [ 5.7  4.4  1.5  0.4]\n",
        " [ 5.4  3.9  1.3  0.4]\n",
        " [ 5.1  3.5  1.4  0.3]\n",
        " [ 5.7  3.8  1.7  0.3]\n",
        " [ 5.1  3.8  1.5  0.3]\n",
        " [ 5.4  3.4  1.7  0.2]\n",
        " [ 5.1  3.7  1.5  0.4]\n",
        " [ 4.6  3.6  1.   0.2]\n",
        " [ 5.1  3.3  1.7  0.5]\n",
        " [ 4.8  3.4  1.9  0.2]\n",
        " [ 5.   3.   1.6  0.2]\n",
        " [ 5.   3.4  1.6  0.4]\n",
        " [ 5.2  3.5  1.5  0.2]\n",
        " [ 5.2  3.4  1.4  0.2]\n",
        " [ 4.7  3.2  1.6  0.2]\n",
        " [ 4.8  3.1  1.6  0.2]\n",
        " [ 5.4  3.4  1.5  0.4]\n",
        " [ 5.2  4.1  1.5  0.1]\n",
        " [ 5.5  4.2  1.4  0.2]\n",
        " [ 4.9  3.1  1.5  0.1]\n",
        " [ 5.   3.2  1.2  0.2]\n",
        " [ 5.5  3.5  1.3  0.2]\n",
        " [ 4.9  3.1  1.5  0.1]\n",
        " [ 4.4  3.   1.3  0.2]\n",
        " [ 5.1  3.4  1.5  0.2]\n",
        " [ 5.   3.5  1.3  0.3]\n",
        " [ 4.5  2.3  1.3  0.3]\n",
        " [ 4.4  3.2  1.3  0.2]\n",
        " [ 5.   3.5  1.6  0.6]\n",
        " [ 5.1  3.8  1.9  0.4]\n",
        " [ 4.8  3.   1.4  0.3]\n",
        " [ 5.1  3.8  1.6  0.2]\n",
        " [ 4.6  3.2  1.4  0.2]\n",
        " [ 5.3  3.7  1.5  0.2]\n",
        " [ 5.   3.3  1.4  0.2]\n",
        " [ 7.   3.2  4.7  1.4]\n",
        " [ 6.4  3.2  4.5  1.5]\n",
        " [ 6.9  3.1  4.9  1.5]\n",
        " [ 5.5  2.3  4.   1.3]\n",
        " [ 6.5  2.8  4.6  1.5]\n",
        " [ 5.7  2.8  4.5  1.3]\n",
        " [ 6.3  3.3  4.7  1.6]\n",
        " [ 4.9  2.4  3.3  1. ]\n",
        " [ 6.6  2.9  4.6  1.3]\n",
        " [ 5.2  2.7  3.9  1.4]\n",
        " [ 5.   2.   3.5  1. ]\n",
        " [ 5.9  3.   4.2  1.5]\n",
        " [ 6.   2.2  4.   1. ]\n",
        " [ 6.1  2.9  4.7  1.4]\n",
        " [ 5.6  2.9  3.6  1.3]\n",
        " [ 6.7  3.1  4.4  1.4]\n",
        " [ 5.6  3.   4.5  1.5]\n",
        " [ 5.8  2.7  4.1  1. ]\n",
        " [ 6.2  2.2  4.5  1.5]\n",
        " [ 5.6  2.5  3.9  1.1]\n",
        " [ 5.9  3.2  4.8  1.8]\n",
        " [ 6.1  2.8  4.   1.3]\n",
        " [ 6.3  2.5  4.9  1.5]\n",
        " [ 6.1  2.8  4.7  1.2]\n",
        " [ 6.4  2.9  4.3  1.3]\n",
        " [ 6.6  3.   4.4  1.4]\n",
        " [ 6.8  2.8  4.8  1.4]\n",
        " [ 6.7  3.   5.   1.7]\n",
        " [ 6.   2.9  4.5  1.5]\n",
        " [ 5.7  2.6  3.5  1. ]\n",
        " [ 5.5  2.4  3.8  1.1]\n",
        " [ 5.5  2.4  3.7  1. ]\n",
        " [ 5.8  2.7  3.9  1.2]\n",
        " [ 6.   2.7  5.1  1.6]\n",
        " [ 5.4  3.   4.5  1.5]\n",
        " [ 6.   3.4  4.5  1.6]\n",
        " [ 6.7  3.1  4.7  1.5]\n",
        " [ 6.3  2.3  4.4  1.3]\n",
        " [ 5.6  3.   4.1  1.3]\n",
        " [ 5.5  2.5  4.   1.3]\n",
        " [ 5.5  2.6  4.4  1.2]\n",
        " [ 6.1  3.   4.6  1.4]\n",
        " [ 5.8  2.6  4.   1.2]\n",
        " [ 5.   2.3  3.3  1. ]\n",
        " [ 5.6  2.7  4.2  1.3]\n",
        " [ 5.7  3.   4.2  1.2]\n",
        " [ 5.7  2.9  4.2  1.3]\n",
        " [ 6.2  2.9  4.3  1.3]\n",
        " [ 5.1  2.5  3.   1.1]\n",
        " [ 5.7  2.8  4.1  1.3]\n",
        " [ 6.3  3.3  6.   2.5]\n",
        " [ 5.8  2.7  5.1  1.9]\n",
        " [ 7.1  3.   5.9  2.1]\n",
        " [ 6.3  2.9  5.6  1.8]\n",
        " [ 6.5  3.   5.8  2.2]\n",
        " [ 7.6  3.   6.6  2.1]\n",
        " [ 4.9  2.5  4.5  1.7]\n",
        " [ 7.3  2.9  6.3  1.8]\n",
        " [ 6.7  2.5  5.8  1.8]\n",
        " [ 7.2  3.6  6.1  2.5]\n",
        " [ 6.5  3.2  5.1  2. ]\n",
        " [ 6.4  2.7  5.3  1.9]\n",
        " [ 6.8  3.   5.5  2.1]\n",
        " [ 5.7  2.5  5.   2. ]\n",
        " [ 5.8  2.8  5.1  2.4]\n",
        " [ 6.4  3.2  5.3  2.3]\n",
        " [ 6.5  3.   5.5  1.8]\n",
        " [ 7.7  3.8  6.7  2.2]\n",
        " [ 7.7  2.6  6.9  2.3]\n",
        " [ 6.   2.2  5.   1.5]\n",
        " [ 6.9  3.2  5.7  2.3]\n",
        " [ 5.6  2.8  4.9  2. ]\n",
        " [ 7.7  2.8  6.7  2. ]\n",
        " [ 6.3  2.7  4.9  1.8]\n",
        " [ 6.7  3.3  5.7  2.1]\n",
        " [ 7.2  3.2  6.   1.8]\n",
        " [ 6.2  2.8  4.8  1.8]\n",
        " [ 6.1  3.   4.9  1.8]\n",
        " [ 6.4  2.8  5.6  2.1]\n",
        " [ 7.2  3.   5.8  1.6]\n",
        " [ 7.4  2.8  6.1  1.9]\n",
        " [ 7.9  3.8  6.4  2. ]\n",
        " [ 6.4  2.8  5.6  2.2]\n",
        " [ 6.3  2.8  5.1  1.5]\n",
        " [ 6.1  2.6  5.6  1.4]\n",
        " [ 7.7  3.   6.1  2.3]\n",
        " [ 6.3  3.4  5.6  2.4]\n",
        " [ 6.4  3.1  5.5  1.8]\n",
        " [ 6.   3.   4.8  1.8]\n",
        " [ 6.9  3.1  5.4  2.1]\n",
        " [ 6.7  3.1  5.6  2.4]\n",
        " [ 6.9  3.1  5.1  2.3]\n",
        " [ 5.8  2.7  5.1  1.9]\n",
        " [ 6.8  3.2  5.9  2.3]\n",
        " [ 6.7  3.3  5.7  2.5]\n",
        " [ 6.7  3.   5.2  2.3]\n",
        " [ 6.3  2.5  5.   1.9]\n",
        " [ 6.5  3.   5.2  2. ]\n",
        " [ 6.2  3.4  5.4  2.3]\n",
        " [ 5.9  3.   5.1  1.8]]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print iris.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
        " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
        " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Move to Pandas Frame for cohesive object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(iris.data,columns=['sepal length','sepal width','petal length','petal width'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Add Flower Type Column to DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_iris = pd.DataFrame(iris.target.astype('O'),columns=['types'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_iris.replace(0,'setosa',inplace=True);\n",
      "df_iris.replace(1,'versicolor',inplace=True);\n",
      "df_iris.replace(2,'virginica',inplace=True);\n",
      "\n",
      "df = df.join(df_iris)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/quasiben/Research/ContinuumDev/Anacondas/INSTALL/lib/python2.7/site-packages/pandas/core/frame.py:3421: FutureWarning: replace with inplace=True  will return None from pandas 0.11 onward\n",
        "  \" from pandas 0.11 onward\", FutureWarning)\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Tools For Plotting Multivariate Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Parallel Coordinates: plot line connecting each measurment/feature "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import parallel_coordinates\n",
      "figure(figsize=(9,6));\n",
      "parallel_coordinates(df,'types')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x107fcb8d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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qPD4fwxJD2V7SQGyYlr6GAI7UtDJveHtwdLb9VU28W1BHVJCaqCA1lY1O5o+I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xXSVO4ilVN5eO8I48k8a2oKCCfzHBlNIosgSyILSx10jiUxSyKRjKE3VOoyY5MF+iJG8mM3Ga27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QEaazbj9DaSkXsBJmMIJtE49FSonrT2NQIBF0ajHU1TKDvwETmFl2MY5HVpaoDO5kN43D2k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\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LraWGMzVncOHyeIGifaKYeeBzDpRkkRzcnIOlBzHYDA32LwkSbcLbUmOt4UzNaewuO0pR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      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##RadViz: Spring tension separtion--spring constant is propotional to numerical value of measurement/feature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import radviz\n",
      "figure(figsize=(9,6));\n",
      "radviz(df, 'types')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/quasiben/Research/ContinuumDev/Anacondas/INSTALL/lib/python2.7/site-packages/pandas/core/frame.py:3576: FutureWarning: rename with inplace=True  will return None from pandas 0.11 onward\n",
        "  \" from pandas 0.11 onward\", FutureWarning)\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1082d5310>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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upGnTpqxcuVK6kYV4ABnzIYSNKYrC0qVLGTt2LN27d2fq1Kl5LqktSi5FUVi/\nfj3jxo2jcuXK/Pjjj/j7+9s6lhA2IQNOhSjBrly5wogRIzh58iQLFiwgMDDQ1pFEAZnNZmbNmsX0\n6dOZNGkSo0ePlnE6otyRAadClECKorBkyRKaNWtGQEAAe/fulcKjjLCzs2PMmDHs3LmT5cuXExQU\nxOnTp20dS4gSRYoPIYpZQkICffv2Zfr06axdu5aPPvqo0FeWFbZXt25dtm7dSt++fWnXrh1fffUV\nFovF1rGEKBGk+BCimNxu7WjatKm0dpQT0goiRO5kzIcQxSAhIYERI0Zw4sQJGduRTwcTDnDq+il8\nXHxp69O21C93f3ssyAcffMCkSZN49dVXZSyIKLNkwKkQNrZ582YGDBhAeHg4kydPli6WfPh5/wLm\nH5iPoiioVWqerN2V8R0m2DpWoTh16hTh4eHY29vz3//+F3d3d1tHEqLQyYBTIWxEURS+/PJLBg4c\nyLJly2RsRz6l6lP5Yd8PZJmy0Jv1ZJoy2Xx2E6evn7J1tEJxeyxIu3btaNOmDQcPHrR1JCGKncbW\nAYQoi/R6PSNGjGDv3r3s3LmTmjVr2jpSqZGqT0Gj1mC0GK3PaVQakjPLzqJidnZ2fPjhhzRp0oQu\nXbowb948nn32WVvHEqLYSPEhRCG7cuUKffv2xcvLi7///ltmKH1I1Sp44ah1JMuUhcKtZluzYsHf\nra6NkxW+0NBQ6tatS9++fTl06BATJ06UcSCiXJB/5UIUor1799KmTRueeuopli9fLoXHI9CoNczu\nMQdvF29UqHBzdOOLp77A1dHV1tGKROvWrdm9ezfr16/nhRdeID093daRhChyMuBUiEKydOlSRo8e\nzXfffcdzzz1n6zg2pTfpmbPnG2Li91DV2ZM3//UmvhWrP/RxzBYzdmq7IkhY8uj1eoYPH86+fftY\nvXq1TLEvSjW520WIImaxWJg0aRKLFy9m1apVNGvWzNaRbO4/f77Drku70Jv1qFDh4uDCsn7Lqayr\n/MjHNFvMKCho1GW3t/j2IOUZM2awbNkyOnToYOtIQjwSudtFiCJkMBjo378///vf/4iOjpbCAzCY\nDWy/sB29WQ+AgoLRbCL6cvQjHU9RFL7YOZOOCzrQcUEHJkSOx2A2FGbkEkOlUjF27FgWLFhAv379\nWLp0qa0jCVEkpPgQ4hFlZWXRt29fTCYTmzZtwsPDw9aRSgS1KufHigoeucXi92O/EXFyNWbFjEWx\n8PeF//GD/05XAAAgAElEQVRdzLcFTFmydevWjS1btvDGG2+wcOFCW8cRotBJ8SHEI8jIyCA4OJgK\nFSqwfPlyHBwcbB2pxNCoNfSu3xt7O3vrYxeHirTzbfdIx9t9eTdZpizrY71Zz+7Luwola0nWuHFj\nIiMjmThxInPnzrV1HCEKVdntPBWiiNy8eZOePXtSs2ZNfvrpJ+zsyseAyPxKN6RzMOGgdXZSR40j\ns5+ejZPW6ZGOV825Ghq1BpPFBIAKFVWdqhZm5BIrICCAqKgounTpQlZWFq+//rqtIwlRKKTlQ4iH\nkJaWRo8ePahfvz7z58+XwiMXc/d+x8XUixgtRiyKhUxTJgv3L3jk4w1pMRQ3RzccNY44ahypYF+B\nsf96s/ACl3B16tRh69atzJo1iy+//NLWcYQoFNLyIUQ+ZWZmEhwcTP369fnuu+9kMqg8nEk+k21A\nqMli4kzy2Uc+nqujK4v7LuXvi39jVky09WmHm6NbYUQtNWrUqEFkZCQdO3bEwcGBESNG2DqSEAUi\nxYcQ+aDX63n22Wfx9vZm3rx5UnjcR0P3hhxOPGQtQOzV9jT0aJhtn5v6m6ToU6hWoVqeA1Hj0+J5\n769JnEs+i7eLN1OD3qeWa60iz19S+fn5sWXLFoKCgtDpdISHh9s6khCPTOb5EOIBDAYD/fr1w8HB\ngSVLlqDRSM1+P3pTFmM3juFo0lFUKhV1XOvwdY87Yz6WHFrMtzFz0Kg16DQ6ZvX4hjpude45hp6Q\n3/qTmJGIRbGgQkVFh4r83n8lzvbOtnhbJcbJkyfp1KkTM2bMICwszNZxhMiVTDImRAEoikJ4eDjX\nrl3j999/x97e3taRSqTlR/7LD/t+wGQx0r1OD8a0G0tieiIKCt4u3tbbb48mHWXUuhHZ7l7xdK7G\nqpDVAOy6tIvJUZNI1aeiQmVd2wXAQe3AJ11n0Na3bbZzG8wGEtMTcdW5lpvC5MiRI3Tp0oVFixbR\ntWtXW8cRIgeZZEyIAvjiiy/Yv38/S5culcIjD1tjo5iz5xtS9SlkGDP449Ra5u2di09FH3wr+mab\n9+P09VM5Xp+YnoDBbCA+LZ5xW94lVZ8KkK3wANBb9Lyz6S0OJtxZgv5o0hF6Ln6agStf4unF3Vlz\nMqKI3mXJ0qhRI5YtW8ZLL73E6dOnbR1HiIcmxYcQedi4cSMzZsxg9erVODuXj7+oH8VfsX+RZc4+\nD8fW81G57uvt4sOtKcfucLZ3xmg2cjTpKGrV/e8eMigG3tr0BmaLGbPFzNiNY7hpuEmmKROD2cDn\nOz7nQsqFgr6lUqFjx45MmzaN4OBgUlNTbR1HiIcixYcQuTh58iQDBw5k+fLlssDXA7jqXLG7p2hw\ncaiY7XFieiLD177CO5vfQqvWYG9nj6PGEQCDycDTi7tzLOkYimJ54Pn0Zj0p+hRuZN3I1n0DoFHb\ncTb5TAHfUekxbNgwOnXqRFhYGGaz2dZxhMg3KT6EuEdKSgrBwcFMnz6dxx9/3NZxSrywJi9Swd4F\nrVqLncoOnUbHmLZjrdvNFjOj1o3gcOJhMk2Z3DTcxGQ2We+GMVgMGMwGfjv6XxpXbWItSvJip7Ij\nJm4PRxKP5JjK3ayY/791pfz48ssvSU9PZ+LEibaOIkS+yYBTIe5iNpvp1asXderUYdasWbaOU2pc\nz7zO5rObMJqNPO7XgZqVa1q3XUm7Qshv/a0LzeXFWevMhA4T0dppmRX9NRdTLuYY9+Fgd2saezu1\nHRbFgovWhVR9KlqNFqPZyAuNQhgROLLQ319Jd/XqVdq0acP06dPlDhhRIjzoui73DApxl3HjxqHX\n65k5c6ato5Qqbo5uvNAoJNdtOo3OOjX6/ZgsZnwrVqdulbrUcfPnlTX/JlWfisFsQIUKnUaHs7YC\nVzOT4P97GLJMWThrnekb8Bx742PYd2Uf60+vp4d/j2zHVhSF7/+Zx9LDS1BQ6FmvF2PajsVOXTZm\nqHV3d2f16tV07tyZevXq0bp1a1tHEuK+pPgQ4v8tWrSI33//nejoaLRara3jlAkmi4mJkeNR3TPI\n9G52Kjs0ag1hTcKoW6UuAF4VvPio8yeM/GM4cOvOl0xTJpmmzByvTzem8/PBOyu/nrp+ErPFTM96\nPa3PRZxYzZLDi61jRNaeXIObzpXwFkML5X2WBE2aNOH777/n2WefJTo6Gi8vL1tHEiJPMuZDCCAm\nJoY333yTiIgIqlSpYus4Zcaq4ys5nHgYk5JXy4eKwc0Gs7DPIl5pNSzblnMpZ+/zurxlmbJYenhx\ntue2no/KNjg1y5TF1vNbH/rYJV2fPn145ZVX6Nu3LwaD4cEvEMJGpPgQ5V5mZiYvvfQSs2bNolGj\nRraOU6acv3E+j7EeKlSoGNxsEIOah+Ns74zZkv1ujfib8TnGfOSX6p6BqG6ObtkGp6pQ4VpG14eZ\nOHEiVapU4cMPP7R1FCHyJN0uotybPHkyTZs25YUXXrB1lDKngUdDdKd01lYHNWoC3AMIbzGEhh6N\nOJRwkK6LuqAAjhodM5/60roOjJujG3YqO8zKw91Cam9nz+Bmg62P0w3ptPP9F1GxUbfGj6hUaNQa\nRrd5rbDeZomiUqmYN28ezZs3p0+fPjRv3tzWkYTIQe52EeXarl276NOnD4cOHcLDw8PWccocRVH4\n8H8fsPH0BuzUdng6e/LN099SxakKCWkJvPDb89laRio6VGRt6Dq0dlouplxg4KoBOebyeJBJT0zm\n6bpPAxB7I5bha1/BZDFhspjxdvEmuH4wQTU7Ua1CtUJ9ryXNzz//zMyZM4mOjpbZeUWxk+nVhchD\nZmYmgwcPZtasWVJ4FBGVSsWEDhNZFRLBr32X8GvfJVRxujWm5kzy6Rwr2hrMBpIykgCoXsmPL5/6\nilqVa1PZoXK+zufu5J7tTpcpUZNJ1aeSbkxHb84iPi0OZ/sKZb7wABgwYAC+vr7S/SJKJCk+RLl1\nu7vl+eeft3WUMs/N0Q1vF+9st7Z6OlfLcQuuRbHgqnO1Pm5WrTmLn1tCd/8e971j5rYalWqiUt3Z\nL+7m5WzjRrJMWVxIOV+Qt1Jq3O5+mTNnDvv377d1HCGykeJDlEu7du1i0aJFfPPNN7aOUm7VcatD\n/0Yv/P/8Hc442DnwzmP/wVGbc4bTlcdX5Gvw6fmU89maemu71sk29btO40j9KvUL5w2UAt7e3nz2\n2WcMGjRI7n4RJUqBi48NGzYQEBBA3bp1+eSTT3Jsj4qKolKlSrRo0YIWLVowffr0gp5SiAKR7paS\nY2TgKL7rOY+JT0xi0bO/8Ey9Z7JtN1lMbI2Nyveg0xuZySw+9Kv18dSgaXhW8MRR44hWraV7nafo\nUuvJQn0PJd2AAQOoXr26dL+IEqVAA07NZjP169fnzz//xMfHh8DAQJYsWUKDBg2s+0RFRTFz5kwi\nIvJe6loGnIri9M4773D+/HmWLVtm6yji/93IuoFFMeOqc7N2m5gsJkavG8WJaycwmA35LkA8naux\nKmS19bHJYuJKWjxOWifcHMvnHC5xcXE0b96cTZs2yd0volgU6fTq0dHR+Pv7U7NmTQBCQkJYvXp1\ntuIDkMJClBi3u1sOHjxo6yiCW4XB+C3j2HVpJwBNPJvyebeZ6DQ6/joXyfGrx8kyZ7/bxVnjTLop\nPc9jqu8ZG6JRa/CtWL3ww5cid3e/xMTEyAy+wuYK1O1y+fJlqle/8z+1r68vly9fzraPSqVix44d\nNGvWjKeffpqjR48W5JRCPDJFUXj99deZMWOGdLeUEIsO/Ez05d0YLUaMFiOHEw8xO/rWgn5XM6/m\nKDwA9Cb9fQefVtRVLLK8pdmAAQOoWrUqP/zwg62jCFGwlo+7R5XnpWXLlly8eBEnJyfWr19Pnz59\nOHnyZI79pkyZYv0+KCiIoKCggkQTIoeVK1diMBhk1c8SZP+V/dnm+TCYDRxKvNUqVc0597VJTJhQ\nocLBziHX2VNPXT+F2WIuM4vGFRaVSsUnn3xCz549GThwIM7OzraOJMqQqKgooqKi8r1/gcZ87Nq1\niylTprBhwwYAPvroI9RqNe+++26er6lVqxZ79+7Fze3O1MYy5kMUNZPJROPGjfnyyy/p3r27reOI\n/zdz5+esOr4So8UI3FpkLqhGELVca/ProV9yXUju9n6fdZvJmhOr2X5hu/X1cKubZevg7dmmUxd3\nhISE0KRJEyZMmGDrKKIMK9JJxlq3bs2pU6eIjY3FYDCwbNkygoODs+2TkJBgDRAdHY2iKNkKDyGK\nw8KFC6lWrRpPPfWUraOIu7zc8hW8Xbxx0jrhrHWmipM7DTwasujgz3kWHg52DvSs15N2vu0Y32Ei\nbo5u1snKdBodoY3DpPC4j/fff58vvviCa9eu2TqKKMcKPL36+vXrGTNmDGazmaFDhzJu3Djmzp0L\nwLBhw/jmm2/49ttv0Wg0ODk5MXPmTNq1a5c9hLR8iCKUmZlJvXr1WL58eY5/e8L2DGYDBxMOYlHM\nNHBvyPPLnyNFn5JtH2etM3Vc/XFzdCPQO5A+DZ61FhgpWSksOriQK2kJtPP9F8/UfSZfXcLl2YgR\nI3B2duazzz6zdRRRRj3oui5ru4gyb8aMGezcuZMVK1bYOop4gKWHlzBr99dYsGR7vq1PO77s/pWN\nUpU98fHxNG7cmH379uHn52frOKIMkrVdRLl248YNPv30Uz744ANbRxH5cPr66RyFhwpVmV2B1la8\nvLwYNmwYU6dOtXUUUU5J8SHKtE8//ZTg4OAcc8+IkinAPQAHO531sQoVLbxaUsetjg1TlU3vvPMO\na9askekPhE1It4sos+Li4mjSpAn79+/PNh+NKLlMFhMTtoxj9+XdqFVq3J3c+faZudaVcHOzNy6G\nGTs+5abhJo/5PsZbj72Ng0aX5/7ijs8++4y///6blStX2jqKKGNkzIcot0aPHo2Dg4MMqitlFEUh\n7mYcerMev0p+1jtZcnMu+RxDIgaTZbo1GZm9nT1P1OjI+51kDan8uD0Ye8WKFQQGBto6jihDinR6\ndSFKqtTUVH799VcOHz5s6yjiIalUKnwq+uRr312XdmKymKyPDWYD289vK6poZY6joyOvvfYas2fP\nZuHChbaOI8oRKT5EmfTLL7/QpUsXvL29bR1FFCGd1hE7lQYTdwoQezt7GyYqfcLDw/H39+fq1au4\nu7vbOo4oJ2TAqShzFEVhzpw5jBw50tZRRBHrVrsblXWV0apvLZSm0+gYGTjKxqlKF3d3d3r37s38\n+fNtHUWUIzLmQ5Q527ZtY9iwYRw9elQmmypl4tPiiTr3FyqVii61uuDhXPWBr0nVp7Ly2AqSs5L5\nl+9jtPVtWwxJy5bdu3cTGhrK6dOnUavlb1JRcDLgVJQ7ISEhtG/fntGjR9s6ingI55LP8e81QzCa\nb63TolVr+bTrZ7T0ailFZBFTFIXWrVszffp0evToYes4ogyQ4kOUK1euXKFBgwbExsZSqVIlW8cR\nD+HtTW/y98W/UbjzWaBGTedanZna6X1Zr6WI/fjjj6xatYo1a9bYOoooA2SGU1Gu/PDDD/Tv318K\nj1LoRtaNbIUHgAUL/7v4N+tPrbNRqvIjNDSUHTt2EBsba+soohyQ4kOUGSaTiblz5zJixAhbRxGP\nIKhWJ3S5TA6WZcrk1PVTNkhUvjg5OTFw4EDrwqBCFCUpPkSZsXbtWvz8/GjevLmto4hHENo4jH4N\nns/RvaLT6Kjj5m+jVOXL8OHD+emnn9Dr9baOIso4KT5EmfHdd99Jq0cpplapGdXmVZY//zvuTu44\naZ1xsNPR1qctT/s/bet45UL9+vVp2rSprAAtipwMOBVlQnJyMjVq1CA+Ph5nZ2dbxxEFpDdlcTb5\nHI5aR2pUqiF3uxSj+fPns27dOpYvX27rKKIUk7tdRLmwZMkSFi9eLCP1hSigxMRE6tWrR0JCAg4O\nDraOI0opudtFlAsREREEBwfbOoYQpV7VqlVp1KgRUVFRto4iyjApPkSpZzQa2bhxIz179rR1FCHK\nhODgYGlFFEVKig9R6m3fvh1/f3+8vLxsHUWIMqFXr15ERERId7goMlJ8iFJPulyEKFwNGjTA3t6e\nAwcO2DqKKKOk+BClmqIoUnwIUchUKhXBwcFERETYOoooo6T4EKXakSNHsFgsNGnSxNZRhChTpPgQ\nRUmKD1Gq3W71kHkghChc7du359y5c1y6dMnWUUQZJMWHKNUiIiLo1auXrWMIUeZotVq6d+/O2rVr\nbR1FlEFSfIhSKy0tjUOHDtGxY0dbRxGiTOrevTuRkZG2jiHKICk+RKm1b98+GjdujL29va2jCFEm\ntW7dmr1799o6hiiDpPgQpdbevXtp1aqVrWMIUWbVq1ePxMREkpOTbR1FlDFSfIhSS4oPIYqWnZ0d\nLVq0kNYPUeik+BCllhQfQhS9Vq1aSfEhCp0UH6JUunnzJufPn6dRo0a2jiJEmSbFhygKUnyIUmn/\n/v00btwYrVZr6yhClGlSfIiiIMWHKJWky0WI4lGvXj2SkpJk0KkoVFJ8iFJJig8hioednR3NmzeX\n1g9RqKT4EKWSFB+ll9liJjkzGbPFbOsoIp+k60UUNik+RKmTnp4ug01LqZi4GLr98iS9l/ai+6/d\n2H9lv60jiXyQ4kMUNik+RKkTGxuLn5+fDDYtZVL1Kbyz+S0yjBkYLUbSDGm8uWks6YZ0W0cTD1Cv\nXj3Onj1r6xiiDJHiQ5Q68fHxeHt72zqGeEjnb1xArbK751kVl29etkkekX/e3t7Ex8fbOoYoQ6T4\nEKVOXFwcXl5eto4hHpKHswdGiyHbcyaLEXenKjZKJPLL09OTxMREzGYZpyMKhxQfotSJj4+X4qMU\nqlahGuHNh6DT6HDSOuNg58CwVsNxc5Tio6TTarW4urqSlJRk6yiijJDiQ1itXr36vh8uUVFRvP32\n2w987lF9//331u8HDx7MkSNHct0vLi5Oul1KqcHNw5nX8wfGd5jAj8HzCWvyoq0jiXzy9vYmLi6u\nyI4fGxvL888//8DnHtXChQsxGo0ATJkyhT/++KNQjisejRQfwmrlypUkJibmuV2lUuXruUc1b968\nfB1XWj5Kt7pV6tKlVhfquNWxdRTxELy8vEr1uI8FCxZgMNzq9ivMzy3xaDS2DiCKVlRUFB9++CE6\nnY4rV67w008/0bhxYzZs2MCHH36I2Wxm9OjRtGvXjo0bN3L06FE6derEgAEDePXVVzEYDLRq1YpZ\ns2ahKMp9z3XvMUNCQhg8eDCOjo6cOXMGZ2dnVq5ciclkIiQkhJSUFOrXr096ejq9e/fmxIkTdO7c\nmZdffhmA2bNnZ3vdbdLyIUTxu7flY9euXYwZMwYnJyeCgoJ477338vwM0Gg0nD9/nipVqrB48WKS\nkpIIDQ3FZDLh6enJsmXLHnj+mJgY3nnnHUwmE7179+bNN99kypQpnD17lmvXrpGens6GDRvQ6XSM\nHDmSI0eO0LZtW/bs2cNHH33E/v376dGjB88++ywAy5YtY86cOdleJ4qPtHyUA5mZmURERPDzzz8z\nYcIEAKZPn05kZCTbtm1j9uzZ+Pn50b17dxYsWMAnn3yCv78/UVFR7Nixg4sXL3L69OkH/rVw7zEt\nFgsqlYr27duzadMmHBwcOHToEKtWrSIgIIDNmzfTrFkzVCoVffr0oX79+kRGRhIaGgqQ43W3ScuH\nEMXv3paPdevWMXnyZCIjI5k0aRKKouT5GdCuXTs2b95MrVq1WL16NW5ubmzevJlt27bh4+NDZGTk\nAz9f/vOf/7By5Uq2bdvG1q1bSUxMRKVSUa9ePf744w/rOWJiYkhJSWHr1q107doVgHbt2tG8eXM2\nbNjA2LFjAXK8ThQvafko41QqFS1atAAgICCA+Ph4kpKSOHnypPV/zJSUFOtYj9utG2fPnuWtt94i\nIyODs2fPPrCv937HvH3+6tWrk5yczJkzZ2jZsiUALVu2ZMeOHbke897X3c4nxYcQxc/Ly4vDhw9b\nH48aNYrp06fz66+/8uKLL9K6des8PwNuz0YcGBjIqVOnuHbtGsOHD+fGjRvExcXRqlUr6tate9/z\nHzx4kD59+gBw48YNLl68COT8nMjIyLCe7/bnTG5y+3wRxUeKjzJOURT27781i+SJEyfw9vbG3d2d\ngIAANm3ahFarxWQyodFo0Gq11lvpvvvuO9588026dOlC79698+xyuf18XseE7P2riqLg7+/Pvn37\n6Nu3L/v27bNuu/cvn3tfB7c+0DQaDRUqVCjoj0YI8RC8vb3ZtGmT9XGlSpWYNWsWBoOB1q1bc+DA\ngTw/A/755x9atGjBnj17CAwMZPHixfTq1YuhQ4fy2muvYbFYHnj+5s2b89tvv1GxYkUsFgtqtZq1\na9dm2+f258vt5+/+fLmdKTcP6lIWhU+KjzJOpVJRqVIlevXqRUJCAj/99BMqlYqJEyfStWtX1Go1\nHh4eLFu2jB49ejBmzBiefPJJevXqxeuvv05AQACKoqBSqaz/vff4t7/uPmbVqlVZunRprnn69OnD\n0qVLefLJJ6ldu7Z1ptJOnTrRp08fwsPDc30dSJeLELZyb7fL3LlzWbFiBSaTifDw8Pt+Buzdu5cl\nS5bg7u7OBx98wKFDhxgwYABr1qzB0dExzy6X258tAB9//DF9+/bFYrHg4OBgHQd292tVKhWtWrWi\nYsWKdOzYkRYtWlg/X4KDg+nfvz/PPfdcrq8TxUullICS7/aFTRS+rVu3snbtWmbMmGHrKNnc/qto\n3rx5pKSk5Pt23T179jBixAhiYmKKOKEQ4m5Hjhyhf//+ed4Cn5fw8HDefvttGjZsWETJcrr9+bJp\n0yZWr17NN998U2znFrc86Lpe4JaPDRs2MGbMGMxmM//+97959913c+zz2muvsX79epycnFiwYIG1\nr00Uj5JY1ffu3Zu0tDR0Ol2+RrrfdndTrhCi+Gg0mjy7LUqaYcOGcebMGRRFYeHChbaOI3KjFIDJ\nZFLq1KmjnDt3TjEYDEqzZs2Uo0ePZtvnjz/+UHr06KEoiqLs2rVLadu2bY7jFDCGKAcsFovy448/\nKv7+/oqzs7MydOhQJT4+3taxhCgXNm/erAQGBio6nU7p2bOncuDAAVtHEiXcg67rBbrVNjo6Gn9/\nf2rWrIlWqyUkJITVq1dn2yciIoJBgwYB0LZtW27cuEFCQkJBTivKoc8//5z33nuPxx57jPDwcI4f\nP85jjz1GVlaWraMJUaZFRkbSv39/atSowbBhwzCbzQQFBXHmzBlbRxOlWIHary9fvkz16tWtj319\nfdm9e/cD97l06RKenp7Z9psyZYr1+6CgIIKCggoSTZQhZrOZjz76iLCwMNzd3QHo2rUry5YtY8WK\nFYSFhdk4obhbRlYGx84dx8HegQa1ArBTZ1/J9mq6gV/+SeBqupFKOg2t3FJIuX6W2j61aVa3iY1S\ni7xMmzaNzp0707hxY+DWH5EZGRl8/fXXfPXVVzZOJ0qKqKgooqKi8r1/gYqP/I4lUO4ZdJLb6+4u\nPoS4W1paGpmZmVSpkn0BMnd3d06fPm2jVOK2uKvxzPzlS65cS6BR7QZsif6LLKMei8VCg5oBfDd+\nNlrNrTsOTBaF73fHk6Y3owDJmSbWn1Oxe913GI3pDH5mAMOee9m2b0hkc/bsWZo2bZrtuWrVqnHi\nxAkbJRIl0b2NBlOnTr3v/gXqdvHx8bFO9AJw8eJFfH1977vPpUuX8PHxKchpRTlTsWJFPD09iY2N\ntT5nsVg4f/48bdq0sV0wQUpaCi9NHMRfe7dy5OxRlm9ZwbXU66RnppOpz+TQmcNMnjuNExfOsvNM\nHKv3nSbLeKvwuENB6+xJlj6Ln9YsJDFZVk4tSdq0aZOj0Dh9+jTt27e3USJRFhSo5aN169acOnWK\n2NhYvL29WbZsGUuWLMm2T3BwMLNnzyYkJIRdu3ZRuXLlHF0uQtyPSqXiyy+/JDw83HoP/+HDh6lb\nty7dunWzdbxybeeh3dZWDsjZymk0GdkSs520Kl3ROd1qubLT6LLPsaC2w6C/CYBWo+V6ynWqunoU\n0zsQD/L+++/z+OOPk5GRgZeXl3UtlVGjRtk6mijFCtTyodFomD17Nk899RQNGzbkhRdeoEGDBsyd\nO5e5c+cC8PTTT1O7dm38/f0ZNmwYc+bMKZTgonzp3bs3f/75J46Ojvzvf//j7bffZt26dajVsjyR\nLV1OiiMzK/O++/jW7Yqjc1U0Wkc0WkcAFIsFi9mIyZhFwoU9pN241TqqVqmpUc2vyHOL/GvQoAH/\n/POPdT2mZ599lr179+Lm5mbraKIUk0nGRKmya9cuXn/99RwDm0XxUxSFzsO7cSMt5b77NWr7Mn51\nu2R7Tp+ZyrmjEaTfvELipTsTxi147wea1W967yFECXD8+HHr6tNCPMiDruvyZ6MoVTQaDUaj0dYx\nBJCWkUZaZvoD97t+5Qgm451bos1mA9euHOTcsbXZCg9Xl8pSeJRgMsGfKExSfIhSpXLlyty4ccPW\nMQTg7OhsvYvlfty9m6G206IoCoqikJWezOE9P2XbR4WKz8d+WlRRRSFITk6mcuXKto4hyggpY0Wp\ncntxKyWXRe5E0Tl98QxboiOxt3cgqGUHvl81nyNnj1ClkhuXEi/n+TrvWo9Tza8t6rvm+nBy8aBp\n22Hs2/6F9blm9ZrSon7zIn0PomBkUUdRmKT4EKWKs7Mz9vb23LhxA1dXV1vHKRf2ndjPyI9fw2A0\noFarmf3fOaCARcm+DLpKraFCJR/MJj0ZN68AUKGSHxqtLvt+KjUePs1xdPYgMz0Je6097wx8s9je\nj3g0cXFxeHt72zqGKCOk+BClzu3WDyk+isfnv3xJluHWmA2L2ZLrPjqnKrR7agoarTNqtR36rFRU\nKjWg5NpKpSgW7DQO2KnVBNSsT4NaAUX9NkQBScuHKEwy5kOUOreLD1E80jLSHrhP08dG4uDohtbe\nCW/FipsAACAASURBVDuNA47O7jg6V8HR2T3HvopiQZ+VSnpqPGaLhSNnjj7wdl1he1J8iMIkxYco\ndby9vYmLi7N1jHKjW7sn0dnf6TrJbaxNRbda2cZ1ZJtE7J79M9OvsXvTFBTFbH3Ozi77+i+i5JFu\nF1GYpNtFlDrS8lG8Xun7b2KO7WXfiQMAtAxoQeJNAzWaD8apggf6rNQc4zryoigWzh2NQJ+ZDIDO\nQUfvjsHYa+2LLL8oHNLyIQqTFB+i1PH29ubChQu2jlFubNixiWOxtyaW8qweiMWtHQ2aB6JWa1Cp\n1Dg6uz/EnUcqajUM5sLJP/Gt6s2Q4MH0CQouuvCi0EjLhyhMUnyIUsfLy0tmOC1GW/ZEkqXPokZA\nD+o1ewGNVpdtEGlehcft2Q3v7YJxdHan6b9eoU8jd57t1Lvo34AosMzMTDIyMmRKdVFoZMyHKHW8\nvb2l26UYuVZ0RaVSUbfp89bulfy0dKhUqlz3U6lU+NTuiOL+L7afvYEhjztoRMlx5coVvLy8ZG4d\nUWik5UOUOl5eXly+nPfEVqJwOemcUBQFtTr7x0VuLRu3n3/gRUqlJuZSGnYq2BeXxsjHfNCo5cJW\nUsXFxcl4D1GopOVDlDo1a9YkPj6e9PQHrysiCkZRFP67+TcArpzfhdmkz7b97iLj9vTpD/PXsVmB\n6xlGTiVlFE5gUSSOHDlC/fr1bR1DlCFSfIhSx97enoYNG3LgwAFbRymTklOTibsaj8ViwaJYMFtu\n3RJ7aPc8Lp7+i8y0JNKSYzEZshcMeXWz5IfeLKtal2R79+6lVatWto4hyhDpdhGlUqtWrdi7dy+P\nPfaYraOUGRaLhanff8D6HRuwU6vxqerL9xPm8ESLx9l5cDd6o55jMQuIPbQMRwcd/9fe/cfVfC9+\nAH99zqkULfnVD8ryY1SEfhDdRU3H75LZZdiMXZsJmzFfWXtc7I7Zhru7/HaXuBfXmltO+uHHyI1U\nfv/Ir0RERRMj4tTpfP/Yde6iklbnfX68no+HB+XT+bxOh3r1fr8/749FM1d06/MBJJkckiSvc/GQ\nALRrXrtLdUmMo0ePYsKECaJjkBHhyAcZJB8fHxw9elR0DKMSn5qAXRm7UVZehkeqx7hacBXz132B\nL6d9gSGvDoJjSwd4dOiC8UPfQknpAxRePYSM3Z/j0ql/Q6Op0K4BeR7pN7/sm5hjkm9rNLXkz0H6\n6vHjxzh79iy6d+8uOgoZEf6PJ4Pk7e2N7777TnQMo3L60hk8evxI+3a5uhznrpyDpYUl/jwpAo/L\nK5B57R52n70G38Hu+Dn/JM4diYZN83bQVKghM6vdLqUONubwe9kWXeybwNKcP//ouzNnzqBDhw5o\n3Lix6ChkRFg+yCB17doVOTk5ePDgAZo0aSI6jlFwae2CRhaN8Fj166JSmSTByc4JAPC4vALfHcjD\nnVI1IG8CaxtrNHnJAc1adca9O7mQm1W/Q+lvF6FqNBW4dSkR3n+Y3PBPiOoF13tQQ+CPHWSQuOi0\n/o0KGgn3dm6wamSFJlZNYGXZGK1sW2LNv/+O9Nzb+KX0yb1Y/re5mE1zF1g3blrtYz599UuFuhzn\nrt/E9Vu8VNpQsHxQQ2D5IIP1ZNEp1Q8Lcwus+2wVVs1djqBe/aFWl2NXxh58v309ohP/haq2ApMk\nCX/s+1o1X0g00FSUV3qPTG4GqyYtcP/B/QZ4BtQQjhw5Ah8fH9ExyMiwfJDBYvmof3KZHN06dkXi\nwSQ8+u/0S1l5GfKvHoFURf0wkwG9X7aBtaUcT+8RZiaT0KJROdRqlfZ9arUKD+7kwKW1S0M+Daon\njx8/xrlz57jYlOodywcZLJaPhvHbvT2euFd8Be3NrsHS7MnaDQ3Kyx5Clb8Pjx6VYPofnNDnZRs0\nNpdB0j4OUGH2EpxsLKDRVKBCXYY7V/dh2eSPYNWIl9YaAi42pYbCBadksDw8PHD58mXcu3cPNjY2\nouMYDblMDv8er+LQ6XSoyn4dtZDJJAz2dEMr25YYMy8ML3tPgUxugYoWvfGXpJNYNKIP+ra3Rca1\n+3hywW2FBnhUpsZID0e0a24JmQRIEnfJNCQZGRmccqEGwZEPMlgWFhbw9/fHrl27REcxSOUVmmr3\n5vhy2hcY7DcQds3t4ObiirURq9C6pSMu5eWgZcdgmDd6CeYWjWFmbgWLJg5IOH3tvx9Z+fE0Gg3K\ny1WQy+q++ymJk5iYiIEDB4qOQUZI0tR2Z6CGDCFJtd6giOi3Vq5cifT0dGzcuFF0FIPxy6NybDhS\niJv3VTCXSxjRtSW6t37puR+XW/wI207eQNHDCshklff0sLMCZgS0x4YjBbh8+xHKKjSoUJeh9MHP\nOJQ0Fx+O+gBvDRnbUE+JGsCDBw/g6OiIa9euwdbWVnQcMjDP+77OkQ8yaMHBwUhMTER5efnzDyYA\nwMYjhbhVooIGgEqtwb9P/4yCe49r/Jib91VYf6QAtx9JkMnkz3xRuVf266jGOC8HvNquKR7evYzr\nOSlIS/4MZWWPsCJmNU5c4GXRhmT37t3o1asXiwc1CJYPMmjOzs5o27YtDh06JDqKQVBXaFB4X4WK\np34gybtbc/k4e/MByn9z87enp1Dk/73UxUwmQdGpOVITP0NW5vcoV/165+EKjRpZl8/WwzMgXYmP\nj0dISIjoGGSkWD7I4AUHB0OpVIqOYRBkEmAhr1wcJAmwblTz1uhmcumZS2mfMJdJ8Hxq2qa5TbNK\nb8tlZnBs6fjigUkItVqNHTt2IDg4WHQUMlIsH2TwQkJCWD5qSZIkjPRoBXOZBAv5r7/a2lrC1a7m\nSyl7tLZGIzOZ9guGXAJsGsnR3MoMfi5NMci1eaXjv5z6xf92Sm1kBd+uPRHg3beBnhXVt8zMTNjZ\n2aFdu3aio5CR4oJTMngajQZOTk7Yu3cvOnfmpZy1cfO+Cnl3H6FJIzk6t2oMWS2uRLn3qBypV+7i\noaoCXR2bwM2u5nvq3Cq+hazLZ2H7ki16dOrOq10MyNy5cyGTybBw4ULRUchAPe/7OssHGYUpU6ag\nffv2mD17tugoRAavS5cuiIqKgq+vr+goZKB4tQuZBE69ENWPnJwc3L59Gz179hQdhYwYywcZhcDA\nQJw6dQpFRUWioxAZNKVSieDgYMhk/PZADYf/usgoWFpaYtiwYdi0aZPoKEQGS6PRIDo6GqNGjRId\nhYwcywcZjbCwMKxcuRIVFVXd/J2qo9FocOPWDVwrvMbPnYk7dOgQSktL0b9/f9FRyMjxxnJkNPz8\n/GBlZYW9e/ciKChIdByDoCpT4aMlM3H84klIkODS+mWsjViFlxpbi45GAqxcuRJTpkzhlAs1OP4L\nI6MhSZJ29INqJ0q5AccvnsRj1WM8Uj1CzvXLWPrPv4qORQLcunULCQkJmDBhgugoZAJYPsiojBs3\nDikpKbh+/broKAYh6/JZPFb9b2v1svIyboNuoqKiovD666+jWbNmzz+Y6Hdi+SCjYm1tjXHjxmHt\n2rWioxiEjs4dYGFuoX3bzMwMHZw6CExEIqjVaqxevRphYWGio5CJYPkgozNlyhSsW7cOKpVKdBS9\n937on9DRqQMaN7JCY8vGcGzhgDnjZ4mORTqWlJQEe3t7eHt7i45CJoI7nJJRCgwMxJQpU3jJYC2U\nq8tx4epFqNVquLp0rjQSQqZhyJAhGDVqFNd7UL3h9upkkmJiYrBixQqkpKSIjkKk13JycuDr64u8\nvDxYWVmJjkNGgturk0kKDQ3FxYsXcebMGdFRiPTamjVrMGHCBBYP0imOfJDRWrRoEU6fPo0tW7aI\njkKkl4qKiuDm5obDhw+jXbt2ouOQEeG0C5mskpISvPLKK0hISICXl5foOER6Z8aMGVCr1YiMjBQd\nhYwMyweZtBUrViA+Ph7JycmioxDpldzcXHh7e+Ps2bOwt7cXHYeMDNd8kEl77733kJ2djX379omO\nQqRX5s2bh6lTp7J4kBB1HvkoLi7G6NGjcfXqVbi4uOCHH36Ara3tM8e5uLjAxsYGcrkc5ubmyMzM\nfDYERz6oAW3ZsgXffvst0tPTIUmS6DhEwp0+fRpBQUHIzs6GjY2N6DhkhBps5GPx4sVQKBS4ePEi\n+vfvj8WLF1cbICUlBcePH6+yeBA1tNGjR0OlUiE2NlZ0FCK9EBERgfDwcBYPEqbOIx+urq7Yv38/\n7O3tUVhYiICAAJw/f/6Z49q1a4cjR46gRYsW1YfgyAc1sOTkZMyYMQNnzpyBmRlv5kym68CBAxg3\nbhwuXLgAS0tL0XHISD3v+3qdvwrfvHlTO1dob2+PmzdvVhsgKCgIcrkckydPxnvvvVflcfPnz9f+\nOSAgAAEBAXWNRvSMgQMHwsHBAdHR0Zg0aZLoOERCaDQahIeHY8GCBSweVK9SUlJeaFPHGkc+FAoF\nCgsLn3n/woUL8c477+DOnTva9zVv3hzFxcXPHFtQUABHR0cUFRVBoVAgMjIS/v7+lUNw5IN0ID09\nHW+88Qays7O5oRKZpPj4eMydOxcnT56EXC4XHYeM2O8a+di9e3e1f/dkusXBwQEFBQWws7Or8jhH\nR0cAQKtWrTBixAhkZmY+Uz6IdKF3797o1asXli1bhoiICNFxiHRKpVIhPDwcixYtYvEg4eq84DQk\nJAQbNmwAAGzYsAGhoaHPHPPw4UPcv38fAPDgwQPs2rULHh4edT0l0e+2bNkyfPvttzh79qzoKEQ6\ntXDhQrRr1w4hISGioxD9vkttR40ahWvXrlW61DY/Px/vvfceEhIScPnyZbz++usAgPLycowbNw5z\n5859NgSnXUiHVq9ejfXr1+PgwYNcfEom4fjx4xg4cCBOnDiB1q1bi45DJoA7nBI9paKiAgqFAgMG\nDMCcOXNExyFqUCqVCj179sSsWbMwfvx40XHIRLB8EFUhNzcXPXv2xP79++Hu7i46DlGDmTdvHo4e\nPYr4+Hhuskc6w/JBVA1Ov5Cx43QLicJ7uxBV4/3334e1tTWWLl0qOgpRvVOpVJgwYQKWLFnC4kF6\nhyMfZNI4/ULGat68eTh27BiUSiWnW0jnOO1C9BycfiFjw+kWEo3TLkTP8WT6pbqbIxIZkocPH3K6\nhfQeRz6IAFy/fh2+vr5Yu3Ythg4dKjoOUZ1oNBqMHTsWcrkc//jHPzjdQsJw5IOoFpycnPDjjz9i\n4sSJOHfunOg4RHXy1Vdf4dKlS1i3bh2LB+k1lg+i/+rTpw+++uorhISEVLppIpEhiI+Px/LlyxEX\nF8cbJ5Le47QL0VNmzpyJM2fOIDExkQtQySBkZWUhMDAQ8fHx8PX1FR2HiNMuRC/q66+/BgDMnj1b\ncBKi57t9+zaGDx+OJUuWsHiQwWD5IHqKmZkZtm7dioSEBERFRYmOQ1StsrIyjBo1CiNGjOB9W8ig\ncNqFqBrnzp1Dv379EBcXBz8/P9FxiJ7x4YcfIjs7Gzt27IBcLhcdh0iL0y5EdeTm5obo6Gi88cYb\nyMvLEx2HqJK///3v2LlzJ7Zs2cLiQQaH5YOoBkOGDMHMmTMxaNAgFBUViY5DBABQKpWIiIiAUqmE\nra2t6DhEL4zlg+g5PvnkE4SGhkKhUKC4uFh0HDJxO3fuxKRJk5CQkIDOnTuLjkNUJ1zzQVQLGo0G\nn3zyCVJTU7F79240bdpUdCQyQfv27cPo0aO5Don0Hm8sR1RPNBoNpk2bhhMnTmDnzp2wtrYWHYlM\nyMGDBxEaGoqYmBgEBASIjkNUIy44JaonkiQhMjIS7u7uGDx4MO7duyc6EpmI1NRUjBgxAps2bWLx\nIKPA8kH0AmQyGdasWQMPDw8oFApuw04N7qeffsLIkSOxefNmDBgwQHQconrB8kH0gmQyGVasWAE/\nPz+89tpr+Pnnn0VHIiOVmJiIMWPGYNu2bQgKChIdh6jesHwQ1YEkSVi2bBkGDx6MgIAAFBQUiI5E\nRiY2NhYTJ06EUqmEv7+/6DhE9Yp3zSKqI0mSsHDhQlhbW8PX1xexsbHw9vYWHYsMnEajwddff43v\nvvsOSUlJ8PLyEh2JqN7xaheierBt2zZ88MEHiIyMxJtvvik6Dhmo0tJSTJo0CRcuXEBcXBycnJxE\nRyKqE17tQqQDI0eOxJ49exAeHo6IiAhUVFSIjkQG5saNG+jbty80Gg1SU1NZPMiosXwQ1ZPu3bsj\nMzMTqampCA0N5aW4VGsZGRnw9fXFyJEjsWnTJlhZWYmORNSgWD6I6pGdnR327NkDR0dH+Pn5IScn\nR3Qk0nMbN25EcHAwVq9ejfDwcEiSJDoSUYPjmg+iBqDRaLBq1SosWLAAmzdvRv/+/UVHIj2jVqsx\nZ84cxMXFQalUwt3dXXQkonrD7dWJBNq7dy/Gjh2LTz/9FNOnT+dPtQQAKC4uxrhx41BWVoYffvgB\nzZs3Fx2JqF5xwSmRQK+99hrS0tKwceNGDBo0CNeuXRMdiQTbvn07PDw80KVLFyQnJ7N4kEli+SBq\nYO3bt0d6ejr69esHb29vrFu3jiN9Jqi4uBhvvfUWZs2ahX/9619YsmQJzMy41RKZJpYPIh0wMzPD\np59+in379mHNmjUcBTExT0Y7WrZsiVOnTnHHUjJ5LB9EOtS1a1eOgpiQp0c7vv32WzRu3Fh0LCLh\nWD6IdIyjIKaBox1E1WP5IBLk6VGQtWvXQq1Wi45Fv1NRURFHO4ieg+WDSKDfjoJER0fDy8sLCQkJ\nnIoxQCUlJfj888/h5uYGOzs7jnYQ1YDlg0gPdO3aFQcPHsTnn3+O//u//0O/fv2QlpYmOhbVgkql\nwvLly/HKK6/gwoULyMjIwLJlyzjaQVQDlg8iPSFJEoYPH45Tp05h4sSJePPNNzF8+HBkZWWJjkZV\nqKiowKZNm+Dq6oqEhAQkJSVh06ZN6NChg+hoRHqP5YNIz8jlckycOBEXL15Ev379EBgYiAkTJuDq\n1auioxF+3To/MTERnp6eiIyMRFRUFJKSktCjRw/R0YgMBssHkZ6ytLTEzJkzkZ2dDWdnZ3h5eeHj\njz9GUVGR6GgmKy0tDQEBAfjkk0+wYMECHDp0CAEBAaJjERkclg8iPde0aVP85S9/QVZWFlQqFTp1\n6oR33nkHGRkZXJiqA6WlpdiwYQN8fX0xduxYvPPOOzh16hRCQ0N5rx6iOuKN5YgMzO3bt7F+/Xqs\nWrUKtra2CAsLw5gxY7jAsZ7l5ORgzZo1WL9+PXx8fBAWFoYhQ4ZALpeLjkak93hXWyIjVVFRgV27\ndmHlypVIS0vD22+/jSlTpqBTp06ioxkstVqNpKQkrFy5EocPH8aECRMwefJkdOzYUXQ0IoPC8kFk\nAnJzc7FmzRpERUWhW7dumDp1KoYNG8Ybl9XSrVu3EBUVhdWrV8Pe3h5hYWEYNWoUrKysREcjMkgs\nH0Qm5PHjx/jxxx+xYsUK5OTkYNiwYQgJCUFQUBCaNGkiOp5euXr1KuLj46FUKpGRkYGRI0ciLCwM\nPj4+oqMRGTyWDyITdeXKFe0318zMTPTt2xchISEYNmwYWrduLTqezlVUVODo0aNQKpVQKpXIz8/X\nljOFQgFra2vREYmMBssHEeHu3btITk6GUqlEcnIyOnbsiJCQEISEhMDDw8Nor9ooLS3FTz/9BKVS\niR07dqBp06ba5927d28uHiVqIA1WPmJiYjB//nycP38ehw8fhpeXV5XHJScnY8aMGVCr1Zg0aRLm\nzJnzwiGJqP6UlZUhNTUV8fHx2L59O9RqNfr06QNvb294e3vDy8sLtra29X7eBw8e4NChQ2jWrBm8\nvLzqvfBoNBpcuXIFR48e1f7KyMiAl5cXgoODERwczMW4RDrSYOXj/PnzkMlkmDx5MpYuXVpl+VCr\n1ejcuTP27NmDNm3aoGfPntiyZQvc3NxeKCQRNQyNRoMLFy4gMzNT+w37xIkTcHBwgI+PT70VkpiY\nGEyaNAkODg64f/8+WrVqhaSkpDpP/zxdNI4cOYJjx47BysqqUu7evXujRYsWdc5NRHXT4NMugYGB\n1ZaPQ4cOYcGCBUhOTgYALF68GAAQHh7+QiGJSHfUajXOnz9faQThSSHp3r072rRpg9atW8PR0bHS\n782aNatyNOPGjRtwc3PDuHHj4ODgAI1Gg/3790Mul2PPnj1VZigrK8PNmzdRUFCA/Pz8Sr9fuXKl\nyqLh7e0NBweHhv70EFEtPO/7eoNeh3fjxg04Oztr33ZyckJGRkaVx86fP1/754CAAG5ZTCSIXC5H\nly5d0KVLF4wfPx7A/wrJmTNntCXgzJkzKCgo0BaDR48ewdHRUVtGWrZsCXNzc2RlZaFDhw7aYiBJ\nEl599VV8/fXXmDJlCuRyOUpLSys91p07d9CyZctKBcfR0RHe3t4YMWIEvLy8WDSI9EhKSgpSUlJq\nfXyN5UOhUKCwsPCZ9y9atAjBwcHPffAXmdP9bfkgIv3y20JSnYcPH6KwsFBbTn7++WeUl5cjPz8f\nd+/erfJj2rZtCxsbGzRq1EhbMBwdHWFnZ8fFoEQG5OlBgwULFtR4fI3lY/fu3b8rTJs2bZCXl6d9\nOy8vD05OTr/rMYlIPzVu3Bjt27dH+/btK73/9ddfh5ubGwoLC7XTLgcOHIC/vz/mzp0rKC0RiVQv\nN5arbl7Hx8cH2dnZyM3NhUqlwtatWxESElIfpySiGmzfvr3Gu9+mpKRg9uzZtX68nTt3Ii4u7pn3\n9+zZEwAQFxenPV90dDRWrFihPaZNmzb4/vvvsXnzZmzatAlr165FcXExNm7cWOvzE5FxqXP5iI2N\nhbOzM9LT0zF06FAMHjwYAJCfn4+hQ4cCAMzMzLB8+XIMHDgQ7u7uGD169DNXuhBR/YuNjcWtW7eq\n/fsXvcx14MCBCA0Nrfbv4+LitOer6rH/+Mc/Ij8/HytXrkR8fDxOnDhhkhudEdGvuMkYkYFISUnB\nokWLYGlpicLCQkRFRaFr165ITk7GokWLoFarMX36dPTu3Rt9+vSBs7MzAgMD8fbbb2PatGlQqVTw\n9vZGZGQkUlJSkJCQgG+++Ub7+BERERg2bBjs7Ozg4eGBX375BZs3b4ZGo4EkSSgpKcHUqVOxePFi\nKJVKeHh44MiRI9i2bVul87m7uyM+Ph4qlQqFhYVQKpVcHEpkYp73fb1epl2ISDdKS0uhVCqxceNG\nREREAAC++OIL7N27F//5z3+wfPlytG3bFoMGDUJ0dDS++uordOzYESkpKUhLS0NeXh4uXbpU5eiE\nv78/UlNTceDAAQQGBuLw4cM4cOAA+vbtqz3m5s2bSE5ORlpaGj7++GPcuXMHLi4ulc6n0Whga2sL\npVKJd999FzExMTr7/BCRYWD5IDIQkiTB09MTAODq6oqCggIUFRXh4sWLUCgUCAoKwi+//KJde/Hk\np47Lly9jyJAhCAgIwLFjx5Cfn1/l4/v5+eHAgQNIS0tDeHg49u/fj5ycnEoLSHNzc9GtWzdthqru\nhyJJEnr06AEAcHZ2xp07d+rvk0BERoH32yYyEBqNBidOnAAAXLhwQbuXhqurK3bt2gVzc3OUl5fD\nzMwM5ubmUKvVAIDVq1dj1qxZ6N+/P4YPH17tUKiNjQ3u37+PJk2a4A9/+AP+/Oc/P7Muw8XFBadP\nn9ZmKCkpAQDtuZ94MrKi0Wg4pUpEz2D5IDIQkiShadOmCA4Oxs2bNxEVFQVJkvDZZ59BoVBAJpOh\nVatW2Lp1KwYPHowZM2YgKCgIwcHB+Oijj+Dq6qpdv/Hk96d169YN5ubmkMlksLKygr+/f6Xz29vb\nY8CAAdp7wTRv3hwAKp2vTZs22seWJMlob1pHRHXHBadEBmL//v3YsWNHpUWiRET6iAtOiYwIRxGI\nyBhw5IOIiIjqFUc+iIiISK+wfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGRTrF8EBER\nkU6xfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGR\nTrF8EBERkU6xfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFO\nsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGRTrF8EBERkU6x\nfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGRTrF8EBERkU6xfBAREZFOsXwQERGRTtW5\nfMTExKBLly6Qy+U4duxYtce5uLigW7du8PT0RK9evep6unqVkpIiOgLVEV87w8bXz3DxtTNs+vb6\n1bl8eHh4IDY2Fn379q3xOEmSkJKSguPHjyMzM7Oup6tX+vYiUO3xtTNsfP0MF187w6Zvr59ZXT/Q\n1dW11sdqNJq6noaIiIiMTIOv+ZAkCUFBQfDx8cG6desa+nRERESk5yRNDcMSCoUChYWFz7x/0aJF\nCA4OBgAEBgZi6dKl8PLyqvIxCgoK4OjoiKKiIigUCkRGRsLf379yCEn6Pc+BiIiI9ExNsx41Trvs\n3r37d5/c0dERANCqVSuMGDECmZmZz5QPTssQERGZjnqZdqmuPDx8+BD3798HADx48AC7du2Ch4dH\nfZySiIiIDFSdy0dsbCycnZ2Rnp6OoUOHYvDgwQCA/Px8DB06FABQWFgIf39/9OjRA76+vhg2bBgG\nDBhQP8mJiIjIINW45sOYzZ49Gzt27ICFhQU6dOiA9evXo2nTpqJjUS3ExMRg/vz5OH/+PA4fPlzt\neiPSL8nJyZgxYwbUajUmTZqEOXPmiI5EtfTuu+8iISEBdnZ2OH36tOg49ALy8vIwfvx43Lp1C5Ik\n4f3338eHH34oOpbp7nA6YMAAZGVl4eTJk+jUqRO+/PJL0ZGolmq7xwzpD7VajWnTpiE5ORlnz57F\nli1bcO7cOdGxqJYmTpyI5ORk0TGoDszNzfHXv/4VWVlZSE9Px4oVK/Ti/57Jlg+FQgGZ7NenJENf\n6gAAAd9JREFU7+vri+vXrwtORLXl6uqKTp06iY5BLyAzMxMdO3aEi4sLzM3N8eabb2L79u2iY1Et\n+fv7o1mzZqJjUB04ODigR48eAABra2u4ubkhPz9fcCoTLh+/FRUVhSFDhoiOQWS0bty4AWdnZ+3b\nTk5OuHHjhsBERKYnNzcXx48fh6+vr+godd/h1BDUZp+ShQsXwsLCAmPHjtV1PKpBbV47Mhzcy4dI\nrJKSErzxxhv429/+Bmtra9FxjLt8PG+fkujoaCQmJuKnn37SUSKqrfrYY4b0R5s2bZCXl6d9Oy8v\nD05OTgITEZmOsrIyjBw5Em+99RZCQ0NFxwFgwtMuycnJ+Oabb7B9+3ZYWlqKjkN1ZKIXaxkcHx8f\nZGdnIzc3FyqVClu3bkVISIjoWERGT6PR4E9/+hPc3d0xY8YM0XG0TLZ8TJ8+HSUlJVAoFPD09ERY\nWJjoSFRL1e0xQ/rLzMwMy5cvx8CBA+Hu7o7Ro0fDzc1NdCyqpTFjxsDPzw8XL16Es7Mz1q9fLzoS\n1dLBgwfxz3/+E/v27YOnpyc8PT314solk93ng4iIiMQw2ZEPIiIiEoPlg4iIiHSK5YOIiIh0iuWD\niIiIdIrlg4iIiHSK5YOIiIh0iuWDiIiIdOr/Aa9PfBQU8LlwAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Observe some separation in the data "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import parallel_coordinates\n",
      "figure(figsize=(9,6));\n",
      "parallel_coordinates(df,'types')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x108657f10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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qPD4fwxJD2V7SQGyYlr6GAI7UtDJveHtwdLb9VU28W1BHVJCaqCA1lY1O5o+I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xXSVO4ilVN5eO8I48k8a2oKCCfzHBlNIosgSyILSx10jiUxSyKRjKE3VOoyY5MF+iJG8mM3Ga27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QEaazbj9DaSkXsBJmMIJtE49FSonrT2NQIBF0ajHU1TKDvwETmFl2MY5HVpaoDO5kN43D2k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\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LraWGMzVncOHyeIGifaKYeeBzDpRkkRzcnIOlBzHYDA32LwkSbcLbUmOt4UzNaewuO0pR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\naXtIAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Returning back to PCA\n",
      "##Goal: Reduce dimensionality!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import PCA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = df[df.columns[:4]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca = PCA(n_components=2, whiten=True).fit(features)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#eigenvectors\n",
      "pca.components_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([[ 0.17650757, -0.04015901,  0.41812992,  0.17516725],\n",
        "       [-1.33840478, -1.48757227,  0.35831476,  0.15229463]])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#eigenvalues\n",
      "pca.explained_variance_ratio_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([ 0.92461621,  0.05301557])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#amount of coverage from pca\n",
      "pca.explained_variance_ratio_.sum()  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "0.97763177502480336"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#project data in pca space (equivalent of np.dot(eigenvector,data)\n",
      "X_pca = pca.transform(features)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Use KMeans on PCA\n",
      "\n",
      "* KMeans: partitions features into K clusters\n",
      "*  Can be difficult to determine number of clusters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cluster import KMeans\n",
      "from numpy.random import RandomState\n",
      "rng = RandomState(42)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmeans = KMeans(3, random_state=rng).fit(X_pca)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmeans.cluster_centers_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([[ 1.01505989, -0.70632886],\n",
        "       [ 0.33475124,  0.89126382],\n",
        "       [-1.287003  , -0.43512572]])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Visualize the clusters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl\n",
      "from itertools import cycle\n",
      "def plot_2D(data, target, target_names):\n",
      "    colors = cycle('rgbcmykw')\n",
      "    target_ids = range(len(target_names))\n",
      "    pl.figure()\n",
      "    for i, c, label in zip(target_ids, colors, target_names):\n",
      "        pl.scatter(data[target == i, 0], data[target == i, 1],\n",
      "                   c=c, label=label)\n",
      "    pl.legend()\n",
      "    pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_2D(X_pca,kmeans.labels_, list(set(df.types.values)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ML9T+/Pnz1KhZI3L2dKa+A/vS48ePX2j/7t27JLeXG0fYg0BcDY4mT51sEe06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\nwkWdMcYsCBd1xhizIFzUGWPMgvwfxQ0SzYNuHTgAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features.ix[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "sepal length    5.1\n",
        "sepal width     3.5\n",
        "petal length    1.4\n",
        "petal width     0.2\n",
        "Name: 0"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_pt = pca.transform(np.array([4.9,3.2,1.3,0.2]))\n",
      "kmeans.predict(new_pt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "array([2], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}