content: """
  =========================================
  SVM: Maximum margin separating hyperplane
  =========================================
  
  Plot the maximum margin separating hyperplane within a two-class
  separable dataset using a Support Vector Machines classifier with
  linear kernel.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import svm
  
  # we create 40 separable points
  np.random.seed(0)
  X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
  Y = [0] * 20 + [1] * 20
  
  # fit the model
  clf = svm.SVC(kernel='linear')
  clf.fit(X, Y)
  
  # get the separating hyperplane
  w = clf.coef_[0]
  a = -w[0] / w[1]
  xx = np.linspace(-5, 5)
  yy = a * xx - (clf.intercept_[0]) / w[1]
  
  # plot the parallels to the separating hyperplane that pass through the
  # support vectors
  b = clf.support_vectors_[0]
  yy_down = a * xx + (b[1] - a * b[0])
  b = clf.support_vectors_[-1]
  yy_up = a * xx + (b[1] - a * b[0])
  
  # plot the line, the points, and the nearest vectors to the plane
  plt.plot(xx, yy, 'k-')
  plt.plot(xx, yy_down, 'k--')
  plt.plot(xx, yy_up, 'k--')
  
  plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
              s=80, facecolors='none')
  plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
  
  plt.axis('tight')
  plt.show()
  