These images how similar features are merged together using feature agglomeration.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | print(__doc__)# Code source: Ga Varoquaux# Modified for documentation by Jaques Grobler# License: BSD 3 clauseimport numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasets, clusterfrom sklearn.feature_extraction.image import grid_to_graphdigits = datasets.load_digits()images = digits.imagesX = np.reshape(images, (len(images), -1))connectivity = grid_to_graph(*images[0].shape)agglo = cluster.FeatureAgglomeration(connectivity=connectivity, n_clusters=32)agglo.fit(X)X_reduced = agglo.transform(X)X_restored = agglo.inverse_transform(X_reduced)images_restored = np.reshape(X_restored, images.shape)plt.figure(1, figsize=(4, 3.5))plt.clf()plt.subplots_adjust(left=.01, right=.99, bottom=.01, top=.91)for i in range(4): plt.subplot(3, 4, i + 1) plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation='nearest') plt.xticks(()) plt.yticks(()) if i == 1: plt.title('Original data') plt.subplot(3, 4, 4 + i + 1) plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16, interpolation='nearest') if i == 1: plt.title('Agglomerated data') plt.xticks(()) plt.yticks(())plt.subplot(3, 4, 10)plt.imshow(np.reshape(agglo.labels_, images[0].shape), interpolation='nearest', cmap=plt.cm.spectral)plt.xticks(())plt.yticks(())plt.title('Labels')plt.show() |
Total running time of the script: (0 minutes 0.758 seconds)
Download Python source code:
plot_digits_agglomeration.py
Download IPython notebook:
plot_digits_agglomeration.ipynb
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