old_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  Ordinary Least Squares and Ridge Regression Variance
  =========================================================
  Due to the few points in each dimension and the straight
  line that linear regression uses to follow these points
  as well as it can, noise on the observations will cause
  great variance as shown in the first plot. Every line's slope
  can vary quite a bit for each prediction due to the noise
  induced in the observations.
  
  Ridge regression is basically minimizing a penalised version
  of the least-squared function. The penalising `shrinks` the
  value of the regression coefficients.
  Despite the few data points in each dimension, the slope
  of the prediction is much more stable and the variance
  in the line itself is greatly reduced, in comparison to that
  of the standard linear regression
  """
  print(__doc__)
  
  
  # Code source: Gaël Varoquaux
  # Modified for documentation by Jaques Grobler
  # License: BSD 3 clause
  
  
  import numpy as np
  import pylab as pl
  
  from sklearn import linear_model
  
  X_train = np.c_[.5, 1].T
  y_train = [.5, 1]
  X_test = np.c_[0, 2].T
  
  np.random.seed(0)
  
  classifiers = dict(ols=linear_model.LinearRegression(),
                     ridge=linear_model.Ridge(alpha=.1))
  
  fignum = 1
  for name, clf in classifiers.iteritems():
      fig = pl.figure(fignum, figsize=(4, 3))
      pl.clf()
      ax = pl.axes([.12, .12, .8, .8])
  
      for _ in range(6):
          this_X = .1 * np.random.normal(size=(2, 1)) + X_train
          clf.fit(this_X, y_train)
  
          ax.plot(X_test, clf.predict(X_test), color='.5')
          ax.scatter(this_X, y_train, s=3, c='.5', marker='o', zorder=10)
  
      clf.fit(X_train, y_train)
      ax.plot(X_test, clf.predict(X_test), linewidth=2, color='blue')
      ax.scatter(X_train, y_train, s=30, c='r', marker='+', zorder=10)
  
      ax.set_xticks(())
      ax.set_yticks(())
      ax.set_ylim((0, 1.6))
      ax.set_xlabel('X')
      ax.set_ylabel('y')
      ax.set_xlim(0, 2)
      fignum += 1
  
  pl.show()
  
new_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  Ordinary Least Squares and Ridge Regression Variance
  =========================================================
  Due to the few points in each dimension and the straight
  line that linear regression uses to follow these points
  as well as it can, noise on the observations will cause
  great variance as shown in the first plot. Every line's slope
  can vary quite a bit for each prediction due to the noise
  induced in the observations.
  
  Ridge regression is basically minimizing a penalised version
  of the least-squared function. The penalising `shrinks` the
  value of the regression coefficients.
  Despite the few data points in each dimension, the slope
  of the prediction is much more stable and the variance
  in the line itself is greatly reduced, in comparison to that
  of the standard linear regression
  """
  print(__doc__)
  
  
  # Code source: Gaël Varoquaux
  # Modified for documentation by Jaques Grobler
  # License: BSD 3 clause
  
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn import linear_model
  
  X_train = np.c_[.5, 1].T
  y_train = [.5, 1]
  X_test = np.c_[0, 2].T
  
  np.random.seed(0)
  
  classifiers = dict(ols=linear_model.LinearRegression(),
                     ridge=linear_model.Ridge(alpha=.1))
  
  fignum = 1
  for name, clf in classifiers.iteritems():
      fig = plt.figure(fignum, figsize=(4, 3))
      plt.clf()
      plt.title(name)
      ax = plt.axes([.12, .12, .8, .8])
  
      for _ in range(6):
          this_X = .1 * np.random.normal(size=(2, 1)) + X_train
          clf.fit(this_X, y_train)
  
          ax.plot(X_test, clf.predict(X_test), color='.5')
          ax.scatter(this_X, y_train, s=3, c='.5', marker='o', zorder=10)
  
      clf.fit(X_train, y_train)
      ax.plot(X_test, clf.predict(X_test), linewidth=2, color='blue')
      ax.scatter(X_train, y_train, s=30, c='r', marker='+', zorder=10)
  
      ax.set_xticks(())
      ax.set_yticks(())
      ax.set_ylim((0, 1.6))
      ax.set_xlabel('X')
      ax.set_ylabel('y')
      ax.set_xlim(0, 2)
      fignum += 1
  
  plt.show()
  
subject: Add plot titles and newstyle plt import in OLS vs Ridge example