64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
"""
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====================================
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Demonstration of k-means assumptions
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====================================
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This example is meant to illustrate situations where k-means will produce
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unintuitive and possibly unexpected clusters. In the first three plots, the
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input data does not conform to some implicit assumption that k-means makes and
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undesirable clusters are produced as a result. In the last plot, k-means
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returns intuitive clusters despite unevenly sized blobs.
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"""
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# Author: Phil Roth <mr.phil.roth@gmail.com>
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# License: BSD 3 clause
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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.datasets import make_blobs
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plt.figure(figsize=(12, 12))
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n_samples = 1500
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random_state = 170
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X, y = make_blobs(n_samples=n_samples, random_state=random_state)
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# Incorrect number of clusters
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y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(X)
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plt.subplot(221)
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plt.scatter(X[:, 0], X[:, 1], c=y_pred)
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plt.title("Incorrect Number of Blobs")
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# Anisotropicly distributed data
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transformation = [[0.60834549, -0.63667341], [-0.40887718, 0.85253229]]
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X_aniso = np.dot(X, transformation)
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y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_aniso)
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plt.subplot(222)
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plt.scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred)
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plt.title("Anisotropicly Distributed Blobs")
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# Different variance
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X_varied, y_varied = make_blobs(
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n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state
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)
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y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied)
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plt.subplot(223)
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plt.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred)
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plt.title("Unequal Variance")
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# Unevenly sized blobs
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X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10]))
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y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_filtered)
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plt.subplot(224)
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plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred)
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plt.title("Unevenly Sized Blobs")
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plt.show()
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