71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
"""
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Demo of affinity propagation clustering algorithm
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====================================================
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Reference:
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Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
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Between Data Points", Science Feb. 2007
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"""
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import numpy as np
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from scikits.learn.clustering import affinity_propagation
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################################################################################
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# Generate sample data
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################################################################################
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np.random.seed(0)
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n_points_per_cluster = 100
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n_clusters = 3
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n_points = n_points_per_cluster*n_clusters
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means = np.array([[1,1],[-1,-1],[1,-1]])
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std = .5
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X = np.empty((0, 2))
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for i in range(n_clusters):
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X = np.r_[X, means[i] + std * np.random.randn(n_points_per_cluster, 2)]
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################################################################################
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# Compute similarities
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################################################################################
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X_norms = np.sum(X*X, axis=1)
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S = - X_norms[:,np.newaxis] - X_norms[np.newaxis,:] + 2 * np.dot(X, X.T)
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p = 10*np.median(S)
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################################################################################
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# Compute Affinity Propagation
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################################################################################
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cluster_centers_indices, labels = affinity_propagation(S, p)
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n_clusters_ = len(cluster_centers_indices)
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print 'Estimated number of clusters: %d' % n_clusters_
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################################################################################
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# Plot result
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################################################################################
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import pylab as pl
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from itertools import cycle
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pl.close('all')
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pl.figure(1)
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pl.clf()
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colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
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for k, col in zip(range(n_clusters_), colors):
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class_members = labels == k
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cluster_center = X[cluster_centers_indices[k]]
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pl.plot(X[class_members,0], X[class_members,1], col+'.')
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pl.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
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markeredgecolor='k', markersize=14)
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for x in X[class_members]:
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pl.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)
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pl.title('Estimated number of clusters: %d' % n_clusters_)
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pl.show()
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