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import numpy as np from matplotlib import pyplot as plt class Perceptron : """An implementation of the perceptron algorithm. Note that this implementation does not include a bias term""" def __init__(self, max_iterations=100, learning_rate=0.2) : self.max_iterations = max_iterations self.learning_rate = learning_rate def fit(self, X, y) : """ Train a classifier using the perceptron training algorithm. After training the attribute 'w' will contain the perceptron weight vector. Parameters ---------- X : ndarray, shape (num_examples, n_features) Training data. y : ndarray, shape (n_examples,) Array of labels. """ self.w = np.zeros(len(X[0])) converged = False iterations = 0 while (not converged and iterations < self.max_iterations) : converged = True for i in range(len(X)) : if y[i] * self.discriminant(X[i]) <= 0 : self.w = self.w + y[i] * self.learning_rate * X[i] converged = False plot_data(X, y, self.w) iterations += 1 self.converged = converged if converged : print ('converged in %d iterations ' % iterations) def discriminant(self, x) : return np.inner(self.w, x) def predict(self, X) : """ make predictions using a trained linear classifier Parameters ---------- X : ndarray, shape (num_examples, n_features) Training data. """ scores = np.inner(self.w, X) return np.sign(scores) def generate_separable_data(N) : w = np.random.uniform(-1, 1, 2) print (w,w.shape) X = np.random.uniform(-1, 1, [N, 2]) print (X,X.shape) y = np.sign(np.dot(X, w)) return X,y,w def plot_data(X, y, w) : fig = plt.figure(figsize=(5,5)) plt.xlim(-1,1) plt.ylim(-1,1) a = -w[0]/w[1] pts = np.linspace(-1,1) plt.plot(pts, a*pts, 'k-') cols = {1: 'r', -1: 'b'} for i in range(len(X)): plt.plot(X[i][0], X[i][1], cols[y[i]]+'o') plt.show() if __name__=='__main__' : X,y,w = generate_separable_data(40) p = Perceptron() p.fit(X,y)