new_contents: from __future__ import print_function
  
  import dit
  import numpy as np
  
  # The functions will import this for you...just make sure you have it.
  import cvxopt
  
  def print_output(d, maxent_dists):
      # Calculate the entropy for each.
      entropies = np.asarray(map(dit.shannon.entropy, maxent_dists))
      print()
      print("Entropies:")
      print(entropies)
  
      # Differences are what we learn at each step.
      netinfo = -1 * np.diff(entropies)
      print()
      print("Network Informations:")
      print(netinfo)
  
      # Total correlation is what is learned at ith from (i-1)th starting at i=2.
      total_corr = netinfo[1:].sum()
      total_corr_true = dit.multivariate.total_correlation(d)
      print()
      print("Total correlation: {0} (numerically)\t {1} (true)".format(total_corr, total_corr_true))
      print()
  
  def example_A():
      """
      Calculate network information using marginal maxentropy.
  
      """
      d = dit.example_dists.Xor()
  
      # Calculate marginal maximum entropy distributions up to order 3.
      maxent_dists = dit.algorithms.marginal_maxent_dists(d, 3)
  
      print_output(d, maxent_dists)
  
      return maxent_dists
  
  def example_B():
      """
      Calculate network information using moment-based maxentropy.
  
      """
      d = dit.example_dists.Xor()
  
      # Calculate moment maximum entropy distributions up to order 3.
      mapping = [-1, 1]
      maxent_dists = dit.algorithms.moment_maxent_dists(d, mapping, 3, with_replacement=False)
  
      print_output(d, maxent_dists)
  
      return maxent_dists
  
  def example_C():
      # Giant bit, perfect correlation.
  
      # Note, doesn't converge if we do this with n=4. e.g.: '1111', '0000'. Lol!
      outcomes = ['111', '000']
      d = dit.Distribution(outcomes, [.5, .5])
      maxent_dists = dit.algorithms.marginal_maxent_dists(d)
      print_output(d, maxent_dists)
  
  if __name__ == '__main__':
      example_A()
  
  
subject: Add an example script for maxent.