content: """
  =========================================
  SGD: Maximum margin separating hyperplane
  =========================================
  
  Plot the maximum margin separating hyperplane within a two-class
  separable dataset using a linear Support Vector Machines classifier
  trained using SGD.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.linear_model import SGDClassifier
  from sklearn.datasets.samples_generator import make_blobs
  
  # we create 50 separable points
  X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)
  
  # fit the model
  clf = SGDClassifier(loss="hinge", alpha=0.01, n_iter=200, fit_intercept=True)
  clf.fit(X, Y)
  
  # plot the line, the points, and the nearest vectors to the plane
  xx = np.linspace(-1, 5, 10)
  yy = np.linspace(-1, 5, 10)
  
  X1, X2 = np.meshgrid(xx, yy)
  Z = np.empty(X1.shape)
  for (i, j), val in np.ndenumerate(X1):
      x1 = val
      x2 = X2[i, j]
      p = clf.decision_function([x1, x2])
      Z[i, j] = p[0]
  levels = [-1.0, 0.0, 1.0]
  linestyles = ['dashed', 'solid', 'dashed']
  colors = 'k'
  plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
  plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
  
  plt.axis('tight')
  plt.show()
  