67 lines
1.6 KiB
Python
67 lines
1.6 KiB
Python
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"""
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=============================================
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A demo of the mean-shift clustering algorithm
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=============================================
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Reference:
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Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
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feature space analysis". IEEE Transactions on Pattern Analysis and
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Machine Intelligence. 2002. pp. 603-619.
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"""
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import numpy as np
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from sklearn.cluster import MeanShift, estimate_bandwidth
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from sklearn.datasets import make_blobs
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# %%
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# Generate sample data
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# --------------------
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centers = [[1, 1], [-1, -1], [1, -1]]
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X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)
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# %%
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# Compute clustering with MeanShift
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# ---------------------------------
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# The following bandwidth can be automatically detected using
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bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)
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ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
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ms.fit(X)
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labels = ms.labels_
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cluster_centers = ms.cluster_centers_
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labels_unique = np.unique(labels)
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n_clusters_ = len(labels_unique)
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print("number of estimated clusters : %d" % n_clusters_)
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# %%
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# Plot result
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# -----------
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import matplotlib.pyplot as plt
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plt.figure(1)
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plt.clf()
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colors = ["#dede00", "#377eb8", "#f781bf"]
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markers = ["x", "o", "^"]
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for k, col in zip(range(n_clusters_), colors):
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my_members = labels == k
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cluster_center = cluster_centers[k]
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plt.plot(X[my_members, 0], X[my_members, 1], markers[k], color=col)
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plt.plot(
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cluster_center[0],
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cluster_center[1],
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markers[k],
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markerfacecolor=col,
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markeredgecolor="k",
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markersize=14,
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)
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plt.title("Estimated number of clusters: %d" % n_clusters_)
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plt.show()
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