content: """
  =====================
  SGD: Weighted samples
  =====================
  
  Plot decision function of a weighted dataset, where the size of points
  is proportional to its weight.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import linear_model
  
  # we create 20 points
  np.random.seed(0)
  X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
  y = [1] * 10 + [-1] * 10
  sample_weight = 100 * np.abs(np.random.randn(20))
  # and assign a bigger weight to the last 10 samples
  sample_weight[:10] *= 10
  
  # plot the weighted data points
  xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
  plt.figure()
  plt.scatter(X[:, 0], X[:, 1], c=y, s=sample_weight, alpha=0.9,
              cmap=plt.cm.bone)
  
  ## fit the unweighted model
  clf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)
  clf.fit(X, y)
  Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
  Z = Z.reshape(xx.shape)
  no_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['solid'])
  
  ## fit the weighted model
  clf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)
  clf.fit(X, y, sample_weight=sample_weight)
  Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
  Z = Z.reshape(xx.shape)
  samples_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['dashed'])
  
  plt.legend([no_weights.collections[0], samples_weights.collections[0]],
             ["no weights", "with weights"], loc="lower left")
  
  plt.xticks(())
  plt.yticks(())
  plt.show()
  