content: #!/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()
  