binary_classification.py

import numpy as np
import matplotlib.pyplot as plt

def __main__():
    x_train = np.array([[10, 50], [20, 30], [25, 30], [20, 60], [15, 70], [40, 40], [30, 45], [20, 45], [40, 30], [7, 35]])
    y_train = np.array([-1, 1, 1, -1, -1, 1 ,1, -1, 1, -1])

    n_train = len(x_train)
    omega = [0, -1]
    a = lambda x : np.sign(x[0] * omega[0] + x[1] * omega[1])
    N = 50
    L = 0.1
    e = 0.1

    last_error_index = -1

    for n in range(N):
        for i in range(n_train):
            if y_train[i] * a(x_train[i]) < 0:
                omega[0] = omega[0] + L * y_train[i]
                last_error_index = i

        Q = sum([1 for i in range(n_train) if y_train[i] * a(x_train[i]) < 0])
        if Q == 0:
            break

    if last_error_index > -1:
        omega[0] = omega[0] + e * y_train[last_error_index]

    print(omega)

    line_x = list(range(max(x_train[:, 0])))
    line_y = [omega[0] * x for x in line_x]

    x_0 = x_train[y_train == 1] 
    x_1 = x_train[y_train == -1]

    plt.scatter(x_0[:, 0], x_0[:, 1], color = 'red')
    plt.scatter(x_1[:, 0], x_1[:, 1], color = 'blue')
    plt.plot(line_x, line_y, color = 'green')

    plt.xlim([0, 45])
    plt.ylim([0, 75])
    plt.ylabel('length')
    plt.xlabel('width')
    plt.grid(True)
    plt.show()


if __name__ == '__main__':
    __main__()