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%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2

Data Generation



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from numpy.random import rand, randn



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n, d, k = 100, 2, 2



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np.random.seed(20)
X = rand(n, d)

# means = [rand(d)  for _ in range(k)]  # works for any k
means = [rand(d) * 0.5 + 0.5 , - rand(d)  * 0.5 + 0.5]  # for better plotting when k = 2

S = np.diag(rand(d))

sigmas = [S]*k # we'll use the same Sigma for all clusters for better visual results

print(means)
print(sigmas)

Solution



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def compute_log_p(X, mean, sigma):
    ''' fill your code in here...
    '''



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log_ps = [compute_log_p(X, m, s) for m, s in zip(means, sigmas)]  # exercise: try to do this without looping



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assignments = np.argmax(log_ps, axis=0)
print(assignments)



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colors = np.array(['red', 'green'])[assignments]
plt.scatter(X[:, 0], X[:, 1], c=colors, s=100)
plt.scatter(np.array(means)[:, 0], np.array(means)[:, 1], marker='*', s=200)