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support_vector_machine.py
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support_vector_machine.py
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import matplotlib.pyplot as plt
import numpy as np
class SupportVectorMachine:
def __init__(self, lr, epochs):
self.weights = None
self.lr = lr
self.epochs = epochs
self.errors = []
def initialize_weights(self, x):
self.weights = np.zeros(len(x[0]))
def fit(self, x, y):
self.initialize_weights(x)
for epoch in range(1, self.epochs):
error = 0
for i, _ in enumerate(x):
if (y[i] * np.dot(x[i], self.weights)) < 1:
self.weights += self.lr * (x[i] * y[i] + (-2 * (1/epoch) * self.weights))
error = 1
else:
self.weights += self.lr * (-2 * (1/epoch) * self.weights)
self.errors.append(error)
def show(self, x):
for d, sample in enumerate(x):
if d < 2:
plt.scatter(sample[0], sample[1], s=120, marker='_', linewidths=2)
else:
plt.scatter(sample[0], sample[1], s=120, marker='+', linewidths=2)
x2 = [self.weights[0], self.weights[1], -self.weights[1], self.weights[0]]
x3 = [self.weights[0], self.weights[1], self.weights[1], -self.weights[0]]
hyperplane = np.array([x2, x3])
X, Y, U, V = zip(*hyperplane)
ax = plt.gca()
ax.quiver(X, Y, U, V, scale=1, color='blue')
plt.show()
def main():
x = np.array([[-2, 4, -1],
[4, 1, -1],
[1, 6, -1],
[2, 4, -1],
[6, 2, -1]])
y = np.array([-1, -1, 1, 1, 1])
svm = SupportVectorMachine(1, 10000)
svm.fit(x, y)
svm.show(x)
if __name__ == '__main__':
main()