content: import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.linear_model import SGDClassifier
  from sklearn.datasets.samples_generator import make_blobs
  
  def plot_sgd_separator():
      # 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'
  
      ax = plt.axes()
      ax.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
      ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
  
      ax.axis('tight')
  
  
  if __name__ == '__main__':
      plot_sgd_separator()
      plt.show()
  