old_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  Linear Regression Example
  =========================================================
  This example uses the only the first feature of the `diabetes` dataset, in
  order to illustrate a two-dimensional plot of this regression technique. The
  straight line can be seen in the plot, showing how linear regression attempts
  to draw a straight line that will best minimize the residual sum of squares
  between the observed responses in the dataset, and the responses predicted by
  the linear approximation.
  
  The coefficients, the residual sum of squares and the variance score are also
  calculated.
  
  """
  print __doc__
  
  
  # Code source: Jaques Grobler
  # License: BSD
  
  
  import pylab as pl
  import numpy as np
  from sklearn import datasets, linear_model
  
  # Load the diabetes dataset
  diabetes = datasets.load_diabetes()
  
  
  # Use only one feature
  diabetes_X = diabetes.data[:, np.newaxis]
  diabetes_X_temp = diabetes_X[:, :, 2]
  
  # Split the data into training/testing sets
  diabetes_X_train = diabetes_X_temp[:-20]
  diabetes_X_test = diabetes_X_temp[-20:]
  
  from sklearn.datasets.samples_generator import make_regression
  
  # this is our test set, it's just a straight line with some
  # gaussian noise
  X, Y = make_regression(n_samples=100, n_features=1, n_informative=1,\
                          random_state=0, noise=35)
  
  
  # Split the targets into training/testing sets
  diabetes_y_train = diabetes.target[:-20]
  diabetes_y_test = diabetes.target[-20:]
  
  # Create linear regression object
  regr = linear_model.LinearRegression()
  
  # Train the model using the training sets
  regr.fit(diabetes_X_train, diabetes_y_train)
  
  # The coefficients
  print 'Coefficients: \n', regr.coef_
  # The mean square error
  print ("Residual sum of squares: %.2f" %
          np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
  # Explained variance score: 1 is perfect prediction
  print ('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))
  
  # Plot outputs
  pl.scatter(diabetes_X_test, diabetes_y_test,  color='black')
  pl.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
          linewidth=3)
  
  pl.xticks(())
  pl.yticks(())
  
  pl.show()
  
new_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  Linear Regression Example
  =========================================================
  This example uses the only the first feature of the `diabetes` dataset, in
  order to illustrate a two-dimensional plot of this regression technique. The
  straight line can be seen in the plot, showing how linear regression attempts
  to draw a straight line that will best minimize the residual sum of squares
  between the observed responses in the dataset, and the responses predicted by
  the linear approximation.
  
  The coefficients, the residual sum of squares and the variance score are also
  calculated.
  
  """
  print __doc__
  
  
  # Code source: Jaques Grobler
  # License: BSD
  
  
  import pylab as pl
  import numpy as np
  from sklearn import datasets, linear_model
  
  # Load the diabetes dataset
  diabetes = datasets.load_diabetes()
  
  
  # Use only one feature
  diabetes_X = diabetes.data[:, np.newaxis]
  diabetes_X_temp = diabetes_X[:, :, 2]
  
  # Split the data into training/testing sets
  diabetes_X_train = diabetes_X_temp[:-20]
  diabetes_X_test = diabetes_X_temp[-20:]
  
  # Split the targets into training/testing sets
  diabetes_y_train = diabetes.target[:-20]
  diabetes_y_test = diabetes.target[-20:]
  
  # Create linear regression object
  regr = linear_model.LinearRegression()
  
  # Train the model using the training sets
  regr.fit(diabetes_X_train, diabetes_y_train)
  
  # The coefficients
  print 'Coefficients: \n', regr.coef_
  # The mean square error
  print ("Residual sum of squares: %.2f" %
          np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
  # Explained variance score: 1 is perfect prediction
  print ('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))
  
  # Plot outputs
  pl.scatter(diabetes_X_test, diabetes_y_test,  color='black')
  pl.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
          linewidth=3)
  
  pl.xticks(())
  pl.yticks(())
  
  pl.show()
  
subject: Clean up unused code.