content: """
  ==============
  Non-linear SVM
  ==============
  
  Perform binary classification using non-linear SVC
  with RBF kernel. The target to predict is a XOR of the
  inputs.
  
  The color map illustrates the decision function learned by the SVC.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import svm
  
  xx, yy = np.meshgrid(np.linspace(-3, 3, 500),
                       np.linspace(-3, 3, 500))
  np.random.seed(0)
  X = np.random.randn(300, 2)
  Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
  
  # fit the model
  clf = svm.NuSVC()
  clf.fit(X, Y)
  
  # plot the decision function for each datapoint on the grid
  Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
  Z = Z.reshape(xx.shape)
  
  plt.imshow(Z, interpolation='nearest',
             extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',
             origin='lower', cmap=plt.cm.PuOr_r)
  contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
                         linetypes='--')
  plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired)
  plt.xticks(())
  plt.yticks(())
  plt.axis([-3, 3, -3, 3])
  plt.show()
  