54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import time
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def generate_data(N):
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y = np.random.choice([1, -1], N)
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x = np.empty((N, 3))
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for index, i in enumerate(y):
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if i == 1:
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mean = [3, 2]
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covariance = [[0.4, 0], [0, 0.4]]
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x1, x2 = np.random.multivariate_normal(mean, covariance)
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x[index] = np.array([1, x1, x2])
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else:
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mean = [5, 0]
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covariance = [[0.6, 0], [0, 0.6]]
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x1, x2 = np.random.multivariate_normal(mean, covariance)
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x[index] = np.array([1, x1, x2])
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return x, y
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def average_square_error(y, y_hat):
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error = (y!=y_hat)
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return error.sum()/error.shape[0]
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if __name__ == '__main__':
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errors = []
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for times in range(128):
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np.random.seed(times)
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train_x, train_y = generate_data(256) # (256, 3), (256, )
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test_x, test_y = generate_data(4096)
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pseudo_inverse_x = np.linalg.pinv(train_x) # (3, 256)
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w = pseudo_inverse_x @ train_y # (3)
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predict_y = train_x @ w
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predict_y = np.sign(predict_y)
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error = average_square_error(predict_y, train_y)
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errors.append(error)
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print(times, error)
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sorted_errors = sorted(errors)
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median = ( sorted_errors[63] + sorted_errors[64] ) / 2
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plt.hist(errors, bins=10)
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plt.xlabel("Ein")
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plt.title("median: {}".format(median))
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plt.savefig("10.png")
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