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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.nipy_spectral)

                plt.xticks(())

                plt.yticks(())

                plt.title('Labels')

                plt.show()