content: """
  ========================================
  Plot multi-class SGD on the iris dataset
  ========================================
  
  Plot decision surface of multi-class SGD on iris dataset.
  The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
  are represented by the dashed lines.
  
  """
  print __doc__
  
  import numpy as np
  import pylab as pl
  from sklearn import datasets
  from sklearn.linear_model import SGDClassifier
  
  # import some data to play with
  iris = datasets.load_iris()
  X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
  y = iris.target
  colors = "bry"
  
  # shuffle
  idx = np.arange(X.shape[0])
  np.random.seed(13)
  np.random.shuffle(idx)
  X = X[idx]
  y = y[idx]
  
  # standardize
  mean = X.mean(axis=0)
  std = X.std(axis=0)
  X = (X - mean) / std
  
  h = .02  # step size in the mesh
  
  clf = SGDClassifier(alpha=0.001, n_iter=100).fit(X, y)
  
  # create a mesh to plot in
  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))
  
  pl.set_cmap(pl.cm.Paired)
  
  # Plot the decision boundary. For that, we will asign a color to each
  # point in the mesh [x_min, m_max]x[y_min, y_max].
  Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
  # Put the result into a color plot
  Z = Z.reshape(xx.shape)
  pl.set_cmap(pl.cm.Paired)
  cs = pl.contourf(xx, yy, Z)
  pl.axis('tight')
  
  # Plot also the training points
  for i, color in zip(clf.classes, colors):
      idx = np.where(y == i)
      pl.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i])
  pl.title("Decision surface of multi-class SGD")
  pl.axis('tight')
  
  # Plot the three one-against-all classifiers
  xmin, xmax = pl.xlim()
  ymin, ymax = pl.ylim()
  coef = clf.coef_
  intercept = clf.intercept_
  
  
  def plot_hyperplane(c, color):
      def line(x0):
          return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
  
      pl.plot([xmin, xmax], [line(xmin), line(xmax)],
              ls="--", color=color)
  
  for i, color in zip(clf.classes, colors):
      plot_hyperplane(i, color)
  pl.legend()
  pl.show()
  