content: """
  ===============================
  Nearest Centroid Classification
  ===============================
  
  Sample usage of Nearest Centroid classification.
  It will plot the decision boundaries for each class.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib.colors import ListedColormap
  from sklearn import datasets
  from sklearn.neighbors import NearestCentroid
  
  n_neighbors = 15
  
  # import some data to play with
  iris = datasets.load_iris()
  # we only take the first two features. We could avoid this ugly
  # slicing by using a two-dim dataset
  X = iris.data[:, :2]
  y = iris.target
  
  h = .02  # step size in the mesh
  
  # Create color maps
  cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
  cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
  
  for shrinkage in [None, .2]:
      # we create an instance of Neighbours Classifier and fit the data.
      clf = NearestCentroid(shrink_threshold=shrinkage)
      clf.fit(X, y)
      y_pred = clf.predict(X)
      print(shrinkage, np.mean(y == y_pred))
      # Plot the decision boundary. For that, we will assign a color to each
      # point in the mesh [x_min, x_max]x[y_min, y_max].
      x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
      y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
      xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                           np.arange(y_min, y_max, h))
      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
  
      # Put the result into a color plot
      Z = Z.reshape(xx.shape)
      plt.figure()
      plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
  
      # Plot also the training points
      plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
                  edgecolor='b', s=20)
      plt.title("3-Class classification (shrink_threshold=%r)"
                % shrinkage)
      plt.axis('tight')
  
  plt.show()
  