Merge pull request #21 from joshiayush/dev - Added "Perceptron" API -…

… "single layer neural network"

CHANGELOG.md

@@ -1,5 +1,20 @@
 # Changelog
 
+## ai — 2024-12-28
+
+### Added
+
+- Added examples on "Perceptron" - single layer neural network ([#659fd15](https://www.github.com/joshiayush/ai/commit/659fd15))
+- Added implementation of "Perceptron" - single layer neural network ([#c6afcc5](https://www.github.com/joshiayush/ai/commit/c6afcc5))
+- Added `statistic` calculating functions to `ai.stats` module (#20) ([#ae05cf1](https://www.github.com/joshiayush/ai/commit/ae05cf1))
+
+## docs — 2024-12-28
+
+### Added
+
+- Added a __Support Vector Machine__ model trained for face recognition ([#912c4c2](https://www.github.com/joshiayush/ai/commit/912c4c2))
+
+
 ## ai — 2024-01-04
 
 ### Added

ai/linear_model/__init__.py

@@ -21,7 +21,9 @@
 
   * `ai.linear_model.linear.LinearRegression`
   * `ai.linear_model.logistic.LogisticRegression`
+  * `ai.linear_model.perceptron.Perceptron`
 """
 
 from .linear import LinearRegression
 from .logistic import LogisticRegression
+from .perceptron import Perceptron

ai/linear_model/perceptron.py

@@ -48,7 +48,7 @@ def __init__(self,
         order to reach to a effecient prediction that minimizes the loss.
       random_state: Seed to generate random weights and bias.
     """
-    self.aplha = alpha
+    self.alpha = alpha
     self.n_iters = n_iters
     self.random_state = random_state
 
@@ -65,16 +65,16 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> 'Perceptron':
     """
     rgen = np.random.RandomState(self.random_state)
     self.weights = rgen.normal(loc=0.0, scale=0.01, size=X.shape[1])
-    self.b = np.float(0.)
-    self.errors = []
+    self.b_ = np.float64(0.)
+    self.errors_ = []
     for _ in range(self.n_iters):
       errors = 0
       for xi, target in zip(X, y):
         update = self.alpha * (target - self.predict(xi))
         self.weights += update * xi
-        self.b += update
+        self.b_ += update
         errors += int(update != 0.0)
-      self.errors.append(errors)
+      self.errors_.append(errors)
     return self
 
   def net_input(self, X):
@@ -87,7 +87,7 @@ def net_input(self, X):
     Returns:
       The dot product of `X` and `weights` plus `b`.
     """
-    return np.dot(X, self.weights) + self.b
+    return np.dot(X, self.weights) + self.b_
 
   def predict(self, X):
     """Return class label after unit step.

examples/linear_model/perceptron.py

@@ -0,0 +1,51 @@
+# Copyright 2023 The AI Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# pylint: disable=too-many-function-args, invalid-name, missing-module-docstring
+# pylint: disable=missing-class-docstring
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from ai.linear_model import Perceptron
+
+df = pd.read_csv(
+    "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
+    header=None,
+    encoding="utf-8",
+)
+print(df.tail())
+
+# select setosa and versicolor
+y = df.iloc[0:100, 4].values
+y = np.where(y == "Iris-setosa", 0, 1)
+
+# extract sepal length and petal length
+X = df.iloc[0:100, [0, 2]].values
+
+# plot data
+plt.scatter(X[:50, 0], X[:50, 1], color="red", marker="o", label="setosa")
+plt.scatter(X[50:100, 0], X[50:100, 1], color="blue", marker="x", label="versicolor")
+plt.xlabel("sepal length [cm]")
+plt.ylabel("petal length [cm]")
+plt.legend(loc="upper left")
+plt.show()
+
+ppn = Perceptron(alpha=0.1, n_iters=10)
+ppn.fit(X, y)
+
+plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker="o")
+plt.xlabel("Epochs")
+plt.ylabel("Number of updates")
+plt.show()