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‎sklearn/metrics/cluster/unsupervised.py

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@@ -162,6 +162,7 @@ def silhouette_samples(X, labels, metric='euclidean', **kwds):
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B = np.array([_nearest_cluster_distance(distances[i], labels, i)
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for i in range(n)])
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sil_samples = (B - A) / np.maximum(A, B)
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# nan values are for clusters of size 1, and should be 0
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return np.nan_to_num(sil_samples)
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