These images how similar features are merged together using feature agglomeration.
print(__doc__) # Code source: Ga Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, cluster from sklearn.feature_extraction.image import grid_to_graph digits = datasets.load_digits() images = digits.images X = 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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