perceptron.py

import numpy as np


def generateData(a, b, num_samples):
    data_x = np.random.uniform(a, b, num_samples)
    data_y = np.random.uniform(a, b, num_samples)
    data = np.array(list(zip(data_x, data_y)))
    labels = np.array([-1 if datap[0] <= datap[1] else 1 for datap in data])
    return data, labels


def evaluate(datap, weights):
    result = datap @ weights.T
    if result > 0:
        return 1
    else:
        return -1


def trainPerceptron(max_iter, data, labels,hinge=False):
    weights = np.zeros(2)
    bias = np.zeros(1)
    for i in range(max_iter):
        for i, x in enumerate(data):
            if hinge:
                act = (x @ weights.T) + bias[0]
            else:
                act = (x @ weights.T)
            y = labels[i]
            if y * act <= 0:
                weights = weights + y * x
                bias = bias + y
    return weights, bias


def predictPerceptron(weights, bias, x, hinge):
    if hinge:
        act = x @ weights.T + bias
    else:
        act = x @ weights.T
    result = np.ones_like(act)
    result[np.where(act <= 0)] = -1
    return result


def mainPerceptron(hinge):
    # hinge = False
    x, y = generateData(0, 1, 20)
    weights, bias = trainPerceptron(1000, x, y, hinge)

    import matplotlib.pyplot as plt
    for i,dp in enumerate(x):
        if y[i] == 1:
            plt.scatter(*dp, color='orange')
        else:
            plt.scatter(*dp, color='blue')


    # plt.show()

    test_x, test_y = generateData(0, 1, 1000)
    result = predictPerceptron(weights,bias,test_x,hinge)
    correct = np.sum(result == test_y)
    accuracy = correct/test_y.shape[0]
    print(accuracy)

if __name__ == "__main__":

    for i in range(20):
        mainPerceptron(hinge=False)