old_contents: from __future__ import absolute_import
  
  import numpy as np 
  from sklearn.datasets import make_sparse_spd_matrix
  from .graphs import Graph 
  
  
  class ErdosRenyiGraph(Graph):
      """Returns the adjacency matrix for Erdos-Renyi network via .create().
  
      Parameters
      ----------- 
      spd_low : float (0, 1)
          Equivalent to make_sparse_spd_matrix `smallest_coef`
  
      spd_high : float (0, 1)
          Equivalent to make_sparse_spd_matrix `largest_coef`
  
      seed : int
          Seed for np.random.RandomState seed. (default=1)
      """
      def __init__(self, spd_low=0.7, spd_high=0.7, **kwargs):
          self.spd_low = spd_low
          self.spd_high = spd_high
          super(ErdosRenyiGraph, self).__init__(**kwargs)
  
      def create(self, n_features, alpha):
          """Build a new graph.
  
          Parameters
          -----------        
          n_features : int 
  
          alpha : float (0,1) 
              # TODO: Better comment on this parameter.
              The complexity / sparsity factor.
          
          Returns
          -----------  
          (n_features, n_features) matrices: covariance, precision, adjacency
          """
          adjacency = make_sparse_spd_matrix(n_features,
                                             alpha=np.abs(1.0 - alpha), 
                                             smallest_coef=self.spd_low,
                                             largest_coef=self.spd_high,
                                             random_state=self.prng)
  
          precision = self.to_precision(adjacency)
          covariance = self.to_covariance(precision)
          return covariance, precision, adjacency
  
new_contents: from __future__ import absolute_import
  
  import numpy as np 
  from sklearn.datasets import make_sparse_spd_matrix
  from .graphs import Graph 
  
  
  class ErdosRenyiGraph(Graph):
      """Returns the adjacency matrix for Erdos-Renyi network via .create().
  
      Parameters
      ----------- 
      spd_low : float (0, 1)
          Equivalent to make_sparse_spd_matrix `smallest_coef`
  
      spd_high : float (0, 1)
          Equivalent to make_sparse_spd_matrix `largest_coef`
  
      seed : int
          Seed for np.random.RandomState seed. (default=1)
      """
      def __init__(self, spd_low=0.7, spd_high=0.7, **kwargs):
          self.spd_low = spd_low
          self.spd_high = spd_high
          super(ErdosRenyiGraph, self).__init__(**kwargs)
  
      def create(self, n_features, alpha):
          """Build a new graph.
  
          Parameters
          -----------        
          n_features : int 
  
          alpha : float (0,1) 
              The complexity / sparsity factor.
              This is (1 - alpha_0) in sklearn.datasets.make_sparse_spd_matrix 
              where alpha_0 is the probability that a coefficient is zero.
          
          Returns
          -----------  
          (n_features, n_features) matrices: covariance, precision, adjacency
          """
          adjacency = make_sparse_spd_matrix(n_features,
                                             alpha=np.abs(1.0 - alpha), 
                                             smallest_coef=self.spd_low,
                                             largest_coef=self.spd_high,
                                             random_state=self.prng)
  
          precision = self.to_precision(adjacency)
          covariance = self.to_covariance(precision)
          return covariance, precision, adjacency
  
subject: Update ER docstring