old_contents: from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect
  from numpy.random import randn
  from shogun.Features import *
  from shogun.Classifier import *
  from shogun.Kernel import *
  import util
  
  util.set_title('SVM')
  util.NUM_EXAMPLES=200
  
  width=5
  
  # positive examples
  pos=util.get_realdata(True)
  plot(pos[0,:], pos[1,:], "r.")
  
  # negative examples
  neg=util.get_realdata(False)
  plot(neg[0,:], neg[1,:], "b.")
  
  # train svm
  labels=util.get_labels()
  train=util.get_realfeatures(pos, neg)
  gk=GaussianKernel(train, train, width)
  svm = LibSVM(10.0, gk, labels)
  svm.train()
  
  x, y, z=util.compute_output_plot_isolines(svm, gk, train)
  pcolor(x, y, z, shading='interp')
  contour(x, y, z, linewidths=1, colors='black', hold=True)
  
  connect('key_press_event', util.quit)
  show()
  
  
new_contents: from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect,axis
  from numpy.random import randn
  from shogun.Features import *
  from shogun.Classifier import *
  from shogun.Kernel import *
  import util
  
  util.set_title('SVM')
  util.NUM_EXAMPLES=200
  
  width=5
  
  # positive examples
  pos=util.get_realdata(True)
  plot(pos[0,:], pos[1,:], "r.")
  
  # negative examples
  neg=util.get_realdata(False)
  plot(neg[0,:], neg[1,:], "b.")
  
  # train svm
  labels=util.get_labels()
  train=util.get_realfeatures(pos, neg)
  gk=GaussianKernel(train, train, width)
  svm = LibSVM(10.0, gk, labels)
  svm.train()
  
  x, y, z=util.compute_output_plot_isolines(svm, gk, train)
  pcolor(x, y, z, shading='interp')
  contour(x, y, z, linewidths=1, colors='black', hold=True)
  axis('tight')
  
  connect('key_press_event', util.quit)
  show()
  
  
subject: use axis tight to improve figure