perceptron

perceptron.py

import numpy as np
from matplotlib.pyplot import  plot

N = 1000
x_p = np.random.rand(N)
y_p = np.random.rand(N)

p = zip(x_p, y_p)

x_m = -np.random.rand(N)
y_m = -np.random.rand(N)

m = zip(x_m, y_m)

plot(x_m, y_m, "*")
plot(x_p, y_p, "o")

w = [np.random.rand(), np.random.rand()]

learning_rate = 0.1

for _ in range(N):
    sign = np.random.randint(0,2)
    if sign:
        point = next(p)
    else:
        point = next(m)
        sign = -1

    if sign * (point[0] * w[0] + point[1] * w[1]) < 0:
        w[0] = w[0] + learning_rate * sign * point[0]
        w[1] = w[1] + learning_rate * sign * point[1]

size = 1.5

plot([0,-w[1]*size], [0, w[0]*size], "blue", linewidth=3)
plot([0,w[1]*size], [0, -w[0]*size], "blue", linewidth=3)