60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
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"""
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=========================================================
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Feature agglomeration
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=========================================================
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These images show how similar features are merged together using
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feature agglomeration.
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"""
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import cluster, datasets
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from sklearn.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.FeatureAgglomeration(connectivity=connectivity, 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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plt.figure(1, figsize=(4, 3.5))
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plt.clf()
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plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.91)
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for i in range(4):
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plt.subplot(3, 4, i + 1)
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plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest")
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plt.xticks(())
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plt.yticks(())
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if i == 1:
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plt.title("Original data")
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plt.subplot(3, 4, 4 + i + 1)
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plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest")
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if i == 1:
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plt.title("Agglomerated data")
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plt.xticks(())
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plt.yticks(())
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plt.subplot(3, 4, 10)
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plt.imshow(
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np.reshape(agglo.labels_, images[0].shape),
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interpolation="nearest",
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cmap=plt.cm.nipy_spectral,
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)
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plt.xticks(())
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plt.yticks(())
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plt.title("Labels")
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plt.show()
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