old_contents: """
  ===================================================================
  Support Vector Regression (SVR) using linear and non-linear kernels
  ===================================================================
  
  Toy example of 1D regression using linear, polynomial and RBF kernels.
  
  """
  print(__doc__)
  
  ###############################################################################
  # Generate sample data
  import numpy as np
  
  X = np.sort(5 * np.random.rand(40, 1), axis=0)
  y = np.sin(X).ravel()
  
  ###############################################################################
  # Add noise to targets
  y[::5] += 3 * (0.5 - np.random.rand(8))
  
  ###############################################################################
  # Fit regression model
  from sklearn.svm import SVR
  
  svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
  svr_lin = SVR(kernel='linear', C=1e3)
  svr_poly = SVR(kernel='poly', C=1e3, degree=2)
  y_rbf = svr_rbf.fit(X, y).predict(X)
  y_lin = svr_lin.fit(X, y).predict(X)
  y_poly = svr_poly.fit(X, y).predict(X)
  
  ###############################################################################
  # look at the results
  import pylab as pl
  pl.scatter(X, y, c='k', label='data')
  pl.hold('on')
  pl.plot(X, y_rbf, c='g', label='RBF model')
  pl.plot(X, y_lin, c='r', label='Linear model')
  pl.plot(X, y_poly, c='b', label='Polynomial model')
  pl.xlabel('data')
  pl.ylabel('target')
  pl.title('Support Vector Regression')
  pl.legend()
  pl.show()
  
new_contents: """
  ===================================================================
  Support Vector Regression (SVR) using linear and non-linear kernels
  ===================================================================
  
  Toy example of 1D regression using linear, polynomial and RBF kernels.
  
  """
  print(__doc__)
  
  import numpy as np
  from sklearn.svm import SVR
  import pylab as pl
  
  ###############################################################################
  # Generate sample data
  X = np.sort(5 * np.random.rand(40, 1), axis=0)
  y = np.sin(X).ravel()
  
  ###############################################################################
  # Add noise to targets
  y[::5] += 3 * (0.5 - np.random.rand(8))
  
  ###############################################################################
  # Fit regression model
  svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
  svr_lin = SVR(kernel='linear', C=1e3)
  svr_poly = SVR(kernel='poly', C=1e3, degree=2)
  y_rbf = svr_rbf.fit(X, y).predict(X)
  y_lin = svr_lin.fit(X, y).predict(X)
  y_poly = svr_poly.fit(X, y).predict(X)
  
  ###############################################################################
  # look at the results
  pl.scatter(X, y, c='k', label='data')
  pl.hold('on')
  pl.plot(X, y_rbf, c='g', label='RBF model')
  pl.plot(X, y_lin, c='r', label='Linear model')
  pl.plot(X, y_poly, c='b', label='Polynomial model')
  pl.xlabel('data')
  pl.ylabel('target')
  pl.title('Support Vector Regression')
  pl.legend()
  pl.show()
  
subject: Update plot_svm_regression.py