24 lines
628 B
Python
24 lines
628 B
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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from sklearn.preprocessing import StandardScaler
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from sklearn.datasets import make_blobs
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from sklearn import metrics
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n_samples = 1500
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x, y = make_blobs(n_samples=n_samples, centers=4, random_state=170)
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x = StandardScaler().fit_transform(x)
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KMeans = KMeans(n_clusters=4, n_init='auto', random_state=170)
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KMeans.fit(x)
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plt.figure(figsize=(12, 6))
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plt.subplot(121)
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plt.scatter(x[:, 0], x[:, 1], c='r')
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plt.title("Before clustering")
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plt.subplot(122)
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plt.scatter(x[:, 0], x[:, 1], c=KMeans.labels_)
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plt.title("After clustering")
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plt.show()
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