58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
"""Mean-shift clustering algorithm
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Reference:
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K. Funkunaga and L.D. Hosteler, "The Estimation of the Gradient of a
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Density Function, with Applications in Pattern Recognition"
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"""
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import numpy as np
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from scikits.learn.meanshift import MeanShift
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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 = 250
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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 = .6
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bandwidth = 1.2
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clustMed = []
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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 clustering with MeanShift =
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# =====================================
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ms = MeanShift(bandwidth=bandwidth)
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ms.fit(X).labels
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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 pylab as pl
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from itertools import cycle
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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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my_members = labels == k
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cluster_center = cluster_centers[k]
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pl.plot(X[my_members,0], X[my_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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pl.title('Estimated number of clusters: %d' % n_clusters_) |