old_contents: """
  =================================================
  SVM: Separating hyperplane for unbalanced classes
  =================================================
  
  Find the optimal separating hyperplane using an SVC for classes that
  are unbalanced.
  
  We first find the separating plane with a plain SVC and then plot
  (dashed) the separating hyperplane with automatically correction for
  unbalanced classes.
  """
  print __doc__
  
  import numpy as np
  import pylab as pl
  from sklearn import svm
  
  # we create 40 separable points
  np.random.seed(0)
  n_samples_1 = 1000
  n_samples_2 = 100
  X = np.r_[1.5 * np.random.randn(n_samples_1, 2),
            0.5 * np.random.randn(n_samples_2, 2) + [2, 2]]
  y = [0] * (n_samples_1) + [1] * (n_samples_2)
  
  # fit the model and get the separating hyperplane
  clf = svm.SVC(kernel='linear', C=1.0)
  clf.fit(X, y)
  
  w = clf.coef_[0]
  a = -w[0] / w[1]
  xx = np.linspace(-5, 5)
  yy = a * xx - clf.intercept_[0] / w[1]
  
  
  # get the separating hyperplane using weighted classes
  wclf = svm.SVC(kernel='linear', class_weight={1: 10})
  wclf.fit(X, y)
  
  ww = wclf.coef_[0]
  wa = -ww[0] / ww[1]
  wyy = wa * xx - wclf.intercept_[0] / ww[1]
  
  # plot separating hyperplanes and samples
  h0 = pl.plot(xx, yy, 'k-', label='no weights')
  h1 = pl.plot(xx, wyy, 'k--', label='with weights')
  pl.scatter(X[:, 0], X[:, 1], c=y, cmap=pl.cm.Paired)
  pl.legend()
  
  pl.axis('tight')
  pl.show()
  
new_contents: """
  =================================================
  SVM: Separating hyperplane for unbalanced classes
  =================================================
  
  Find the optimal separating hyperplane using an SVC for classes that
  are unbalanced.
  
  We first find the separating plane with a plain SVC and then plot
  (dashed) the separating hyperplane with automatically correction for
  unbalanced classes.
  """
  print __doc__
  
  import numpy as np
  import pylab as pl
  from sklearn import svm
  
  # we create 40 separable points
  rng = np.random.RandomState(0)
  n_samples_1 = 1000
  n_samples_2 = 100
  X = np.r_[1.5 * rng.randn(n_samples_1, 2),
            0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
  y = [0] * (n_samples_1) + [1] * (n_samples_2)
  
  # fit the model and get the separating hyperplane
  clf = svm.SVC(kernel='linear', C=1.0)
  clf.fit(X, y)
  
  w = clf.coef_[0]
  a = -w[0] / w[1]
  xx = np.linspace(-5, 5)
  yy = a * xx - clf.intercept_[0] / w[1]
  
  
  # get the separating hyperplane using weighted classes
  wclf = svm.SVC(kernel='linear', class_weight={1: 10})
  wclf.fit(X, y)
  
  ww = wclf.coef_[0]
  wa = -ww[0] / ww[1]
  wyy = wa * xx - wclf.intercept_[0] / ww[1]
  
  # plot separating hyperplanes and samples
  h0 = pl.plot(xx, yy, 'k-', label='no weights')
  h1 = pl.plot(xx, wyy, 'k--', label='with weights')
  pl.scatter(X[:, 0], X[:, 1], c=y, cmap=pl.cm.Paired)
  pl.legend()
  
  pl.axis('tight')
  pl.show()
  
subject: FIX : use RandomState rather than global seed