Perceptron-1.py

import numpy as np
class Perceptron(object):

    def __init__(self, eta = 0.01, n_iter = 10):
        self.eta = eta
        self.n_iter = n_iter
    def fit(self, X, y):
        self.w_ = np.zeros(1 + X.shape[1])

        for _ in range(self.n_iter):

            for xi, target in zip(X, y):
                 error = target-self.predict(xi)
                 if error!=0:
                    update = self.eta * (self.predict(xi))
                    self.w_[1:] += update * xi
                    self.w_[0] += update
        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 stop"""
        return np.where(self.net_input(X) >= 0.0, 1, -1)

X = np.array([[0,0,0],[0,0,1],[0,1,0],[0,1,1],[1,0,0],[1,0,1],[1,1,0],[1,1,1]])
print('Inputs : \n',X)
y = np.array([-1,-1,-1,-1,1,1,1,1])
ppn = Perceptron(eta=0.1, n_iter=10)
ppn.fit(X, y)
print('Outputs', ppn.predict(X))
print('Output for [1,1,1] is',ppn.predict([1,1,1]))
print('Output for [0,0,0] is',ppn.predict([0,0,0]))