new_contents: """
  ================================================
  SVM: Separating hyperplane with weighted classes
  ================================================
  
  """
  
  import numpy as np
  import pylab as pl
  from scikits.learn import svm
  
  # we create 40 separable points
  np.random.seed(0)
  nsamples_1 = 1000
  nsamples_2 = 100
  X = np.r_[1.5*np.random.randn(nsamples_1, 2), 0.5*np.random.randn(nsamples_2, 2) + [2, 2]]
  Y = [0]*(nsamples_1) + [1]*(nsamples_2)
  
  # fit the model and get the separating hyperplane
  clf = svm.SVC(kernel='linear')
  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')
  wclf.fit(X, Y, {1: 10})
  
  ww = wclf.coef_[0]
  wa = -ww[0]/ww[1]
  wyy = wa*xx - (wclf.intercept_[0])/ww[1]
  
  # plot separating hyperplanes and samples
  pl.set_cmap(pl.cm.Paired)
  pl.plot(xx, yy, 'k-')
  pl.plot(xx, wyy, 'k--')
  pl.scatter(X[:,0], X[:,1], c=Y)
  
  pl.axis('tight')
  pl.show()
  
  
subject: Add an exmple of svm using weighted classes