47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
import numpy as np
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import pylab as pl
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from scikits.learn import datasets, cluster
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from scikits.learn.feature_extraction.image import grid_to_graph
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digits = datasets.load_digits()
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images = digits.images
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X = np.reshape(images, (len(images), -1))
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connectivity = grid_to_graph(*images[0].shape)
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agglo = cluster.WardAgglomeration(connectivity=connectivity,
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n_clusters=32)
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agglo.fit(X)
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X_reduced = agglo.transform(X)
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X_restored = agglo.inverse_transform(X_reduced)
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images_restored = np.reshape(X_restored, images.shape)
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pl.figure(1, figsize=(4, 3.5))
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pl.clf()
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pl.subplots_adjust(left=.01, right=.99, bottom=.01, top=.91)
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for i in range(4):
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pl.subplot(3, 4, i+1)
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pl.imshow(images[i], cmap=pl.cm.gray,
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vmax=16, interpolation='nearest')
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pl.xticks(())
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pl.yticks(())
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if i == 1:
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pl.title('Original data')
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pl.subplot(3, 4, 4+i+1)
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pl.imshow(images_restored[i],
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cmap=pl.cm.gray, vmax=16, interpolation='nearest')
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if i == 1:
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pl.title('Agglomerated data')
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pl.xticks(())
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pl.yticks(())
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pl.subplot(3, 4, 10)
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pl.imshow(np.reshape(agglo.labels_, images[0].shape),
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interpolation='nearest', cmap=pl.cm.spectral)
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pl.xticks(())
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pl.yticks(())
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pl.title('Labels')
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