new_contents: """
  Sample usage of Support Vector Machines to classify a sample.
  It will plot the decision surface and the support vectors.
  """
  import numpy as np
  import pylab as pl
  from scikits.learn import neighbors, datasets
  
  # import some data to play with
  iris = datasets.load('iris')
  X = iris.data[:, :2] # we only take the first two features. We could
                       # avoid this ugly slicing by using a two-dim dataset
  Y = iris.target
  
  h=.02 # step size in the mesh
  
  # we create an instance of SVM and fit out data. We do not scale our
  # data since we want to plot the support vectors
  clf = neighbors.Neighbors()
  clf.fit(X, Y)
  
  # Plot the decision boundary. For that, we will asign a color to each
  # point in the mesh [x_min, m_max]x[y_min, y_max].
  x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
  y_min, y_max = X[:,1].min()-1, X[:,1].max()+1
  xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
  Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
  
  # Put the result into a color plot
  Z = Z.reshape(xx.shape)
  pl.set_cmap(pl.cm.Paired)
  pl.pcolormesh(xx, yy, Z)
  
  # Plot also the training points
  pl.scatter(X[:,0], X[:,1], c=Y)
  # and the support vectors
  pl.title('3-Class classification using Nearest Neighbors')
  pl.axis('tight')
  pl.show()
  
subject: Add neighbors example