and.py

import numpy as np
import matplotlib.pyplot as plt
X = np.array([
    [0,0,-1],
    [0,1,-1],
    [1,0,-1],
    [1,1,-1],
    

])

y = np.array([-1,-1,-1,1])

def perceptron_sgd(X, Y):
    w = np.zeros(len(X[0]))
    eta = 1
    epochs = 20

    for t in range(epochs):
        for i, x in enumerate(X):
            if (np.dot(X[i], w)*Y[i]) <= 0:
                w = w + eta*X[i]*Y[i]

    return w

w = perceptron_sgd(X,y)
print(w)
for d, sample in enumerate(X):
    # Plot the negative samples
    if d < 2:
        plt.scatter(sample[0], sample[1], s=120, marker='_', linewidths=2)
    # Plot the positive samples
    else:
        plt.scatter(sample[0], sample[1], s=120, marker='+', linewidths=2)

# Print a possible hyperplane, that is seperating the two classes.
plt.plot([-2,6],[6,0.5])

def perceptron_sgd_plot(X, Y):
    '''
    train perceptron and plot the total loss in each epoch.
    
    :param X: data samples
    :param Y: data labels
    :return: weight vector as a numpy array
    '''
    w = np.zeros(len(X[0]))
    eta = 1
    n = 30
    errors = []

    for t in range(n):
        total_error = 0
        for i, x in enumerate(X):
            if (np.dot(X[i], w)*Y[i]) <= 0:
                total_error += (np.dot(X[i], w)*Y[i])
                w = w + eta*X[i]*Y[i]
        errors.append(total_error*-1)
        
    plt.plot(errors)
    plt.xlabel('Epoch')
    plt.ylabel('Total Loss')
    
    return w
perceptron_sgd_plot(X,y)