54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
"""
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===========================================================
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A demo of hierarchical clustering - structured ward
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===========================================================
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Example builds a swiss roll dataset and runs the hierarchical
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clustering on k-Nearest Neighbors graph. It's a hierarchical
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clustering with structure prior.
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"""
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# Authors : Vincent Michel, 2010
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# Alexandre Gramfort, 2010
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# License: BSD
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print __doc__
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import time as time
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import numpy as np
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import pylab as pl
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import mpl_toolkits.mplot3d.axes3d as p3
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from scikits.learn.neighbors import kneighbors_graph
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from scikits.learn.cluster import Ward
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from scikits.learn.datasets.samples_generator import swiss_roll
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###############################################################################
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# Generate data (swiss roll dataset)
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n_samples = 5000
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noise = 0.05
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X = swiss_roll(n_samples, noise)
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###############################################################################
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# Define the structure A of the data. Here a 10 nearest neighbors
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connectivity = kneighbors_graph(X, n_neighbors=10)
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###############################################################################
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# Compute clustering
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print "Compute structured hierarchical clustering..."
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st = time.time()
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ward = Ward(n_clusters=10).fit(X, connectivity=connectivity)
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label = ward.labels_
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print "Elapsed time: ", time.time() - st
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print "Number of points: ", label.size
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print "Number of clusters: ", np.unique(label).size
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###############################################################################
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# Plot result
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fig = pl.figure()
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ax = p3.Axes3D(fig)
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ax.view_init(7, -80)
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for l in np.unique(label):
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ax.plot3D(X[label == l, 0], X[label == l, 1], X[label == l, 2],
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'o', color=pl.cm.jet(float(l) / np.max(label + 1)))
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pl.show()
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