old_contents: """
  ==========================
  SGD: Convex Loss Functions
  ==========================
  
  Plot the convex loss functions supported by
  `sklearn.linear_model.stochastic_gradient`.
  """
  print __doc__
  
  import numpy as np
  import pylab as pl
  from sklearn.linear_model.sgd_fast import Hinge, ModifiedHuber, SquaredLoss
  
  ###############################################################################
  # Define loss funcitons
  xmin, xmax = -3, 3
  hinge = Hinge(1)
  log_loss = lambda z, p: np.log2(1.0 + np.exp(-z))
  modified_huber = ModifiedHuber()
  squared_loss = SquaredLoss()
  
  ###############################################################################
  # Plot loss funcitons
  xx = np.linspace(xmin, xmax, 100)
  pl.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-',
          label="Zero-one loss")
  pl.plot(xx, [hinge.loss(x, 1) for x in xx], 'g-',
          label="Hinge loss")
  pl.plot(xx, [log_loss(x, 1) for x in xx], 'r-',
          label="Log loss")
  pl.plot(xx, [modified_huber.loss(x, 1) for x in xx], 'y-',
          label="Modified huber loss")
  #pl.plot(xx, [2.0*squared_loss.loss(x, 1) for x in xx], 'c-',
  #        label="Squared loss")
  pl.ylim((0, 5))
  pl.legend(loc="upper right")
  pl.xlabel(r"$y \cdot f(x)$")
  pl.ylabel("$L(y, f(x))$")
  pl.show()
  
new_contents: """
  ==========================
  SGD: Convex Loss Functions
  ==========================
  
  Plot the convex loss functions supported by
  `sklearn.linear_model.stochastic_gradient`.
  """
  print __doc__
  
  import numpy as np
  import pylab as pl
  from sklearn.linear_model.sgd_fast import Hinge, ModifiedHuber, SquaredLoss
  
  ###############################################################################
  # Define loss funcitons
  xmin, xmax = -3, 3
  hinge = Hinge(1)
  perceptron = Hinge(0)
  log_loss = lambda z, p: np.log2(1.0 + np.exp(-z))
  modified_huber = ModifiedHuber()
  squared_loss = SquaredLoss()
  
  ###############################################################################
  # Plot loss funcitons
  xx = np.linspace(xmin, xmax, 100)
  pl.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-',
          label="Zero-one loss")
  pl.plot(xx, [hinge.loss(x, 1) for x in xx], 'g-',
          label="Hinge loss")
  pl.plot(xx, [perceptron.loss(x, 1) for x in xx], 'm-',
          label="Perceptron loss")
  pl.plot(xx, [log_loss(x, 1) for x in xx], 'r-',
          label="Log loss")
  pl.plot(xx, [modified_huber.loss(x, 1) for x in xx], 'y-',
          label="Modified huber loss")
  #pl.plot(xx, [2.0*squared_loss.loss(x, 1) for x in xx], 'c-',
  #        label="Squared loss")
  pl.ylim((0, 5))
  pl.legend(loc="upper right")
  pl.xlabel(r"$y \cdot f(x)$")
  pl.ylabel("$L(y, f(x))$")
  pl.show()
  
subject: Add perceptron loss to plot.