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- #!/usr/bin/python
- # -*- coding: utf-8 -*-
- """
- =========================================================
- Feature agglomeration
- =========================================================
- These images how similar features are merged together using
- feature agglomeration.
- """
- print(__doc__)
- # Code source: Gaël 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()
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