content: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  Sparsity Example: Fitting only features 1  and 2
  =========================================================
  
  Features 1 and 2 of the diabetes-dataset are fitted and
  plotted below. It illustrates that although feature 2
  has a strong coefficient on the full model, it does not
  give us much regarding `y` when compared to just feature 1
  
  """
  print(__doc__)
  
  
  # Code source: Gaël Varoquaux
  # Modified for documentation by Jaques Grobler
  # License: BSD 3 clause
  
  import matplotlib.pyplot as plt
  import numpy as np
  from mpl_toolkits.mplot3d import Axes3D
  
  from sklearn import datasets, linear_model
  
  diabetes = datasets.load_diabetes()
  indices = (0, 1)
  
  X_train = diabetes.data[:-20, indices]
  X_test = diabetes.data[-20:, indices]
  y_train = diabetes.target[:-20]
  y_test = diabetes.target[-20:]
  
  ols = linear_model.LinearRegression()
  ols.fit(X_train, y_train)
  
  
  ###############################################################################
  # Plot the figure
  def plot_figs(fig_num, elev, azim, X_train, clf):
      fig = plt.figure(fig_num, figsize=(4, 3))
      plt.clf()
      ax = Axes3D(fig, elev=elev, azim=azim)
  
      ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+')
      ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]),
                      np.array([[-.1, .15], [-.1, .15]]),
                      clf.predict(np.array([[-.1, -.1, .15, .15],
                                            [-.1, .15, -.1, .15]]).T
                                  ).reshape((2, 2)),
                      alpha=.5)
      ax.set_xlabel('X_1')
      ax.set_ylabel('X_2')
      ax.set_zlabel('Y')
      ax.w_xaxis.set_ticklabels([])
      ax.w_yaxis.set_ticklabels([])
      ax.w_zaxis.set_ticklabels([])
  
  #Generate the three different figures from different views
  elev = 43.5
  azim = -110
  plot_figs(1, elev, azim, X_train, ols)
  
  elev = -.5
  azim = 0
  plot_figs(2, elev, azim, X_train, ols)
  
  elev = -.5
  azim = 90
  plot_figs(3, elev, azim, X_train, ols)
  
  plt.show()
  