wehub-resource-sync
47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
import random
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import datasets
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from mla.kmeans import KMeans
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from mla.gaussian_mixture import GaussianMixture
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random.seed(1)
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np.random.seed(6)
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def make_clusters(skew=True, *arg, **kwargs):
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X, y = datasets.make_blobs(*arg, **kwargs)
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if skew:
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nrow = X.shape[1]
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for i in np.unique(y):
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X[y == i] = X[y == i].dot(np.random.random((nrow, nrow)) - 0.5)
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return X, y
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def KMeans_and_GMM(K):
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COLOR = "bgrcmyk"
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X, y = make_clusters(skew=True, n_samples=1500, centers=K)
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_, axes = plt.subplots(1, 3)
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# Ground Truth
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axes[0].scatter(X[:, 0], X[:, 1], c=[COLOR[int(assignment)] for assignment in y])
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axes[0].set_title("Ground Truth")
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# KMeans
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kmeans = KMeans(K=K, init="++")
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kmeans.fit(X)
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kmeans.predict()
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axes[1].set_title("KMeans")
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kmeans.plot(ax=axes[1], holdon=True)
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# Gaussian Mixture
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gmm = GaussianMixture(K=K, init="kmeans")
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gmm.fit(X)
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axes[2].set_title("Gaussian Mixture")
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gmm.plot(ax=axes[2])
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if __name__ == "__main__":
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KMeans_and_GMM(4)
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