Perceptron algorithm

perceptron/perceptron.py

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+import numpy as np
+class Perceptron(object):
+    """
+    Perceptron classifier
+    ___________________
+    parameters
+    __________________
+    eta: float
+    learning rate betweeen 0.0 and 1.0
+    n_iter: int
+    Passes over the training dataset
+
+    ___________________
+    Attributes
+    ___________________
+    w_: 1d array
+    weights after training
+    errors_: list
+    Number of msiclassifications for every epoch
+    """
+    def __init__(self, eta, n_iter):
+        self.eta = eta
+        self.n_iter = n_iter
+    
+    def fit(self, X, y):
+        """
+        Fit training data
+        ______________________
+        parameters
+        ______________________
+        X: {array-like}, shape = [n_samples, n_features]
+            Training vectors where n_samples is the number of samples
+            and n_features is the number of features
+        y: array-like, shape = [n_samples]
+            Target values
+
+        ____________________
+        Returns
+        ____________________
+        self: object
+        """
+        self.w_ = np.zeros(1 + X.shape[1])
+        self.errors_ = []
+
+        for _ in range(self.n_iter):
+            errors = 0
+            for xi,target in zip(X, y):
+                update = self.eta * (target - self.predict(xi))
+                self.w_[1:] += update * xi
+                self.w_[0] += update
+                errors += int(update != 0.0)
+            self.errors_.append(errors)
+        return self
+    
+    def net_input(self, X):
+        """
+        Calculate net input
+        """
+        return np.dot(X, self.w_[1:]) + self.w_[0]
+
+    def predict(self, X):
+        """
+        Return class label after unit step
+        """
+        return np.where(self.net_input(X) >= 0.0, 1, -1)