class_docstr: 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`
  
  n_blocks : int (default=2)
      Number of blocks.  Returned matrix will be square with
      shape n_block_features * n_blocks.
  
  chain_blocks : bool (default=True)
      Apply random lattice structure to chain blocks together.
  
  seed : int
      Seed for np.random.RandomState seed. (default=1)
docstr: Build a new graph.
  
  Doc for ".create(n_features, alpha)"
  
  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
docstr_tok: ["Build","a","new","graph",".","Doc","for","``",".create","(","n_features",",","alpha",")","''","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"]
code: def prototype_adjacency(self, n_block_features, alpha):
      """Build a new graph.
  
      Doc for ".create(n_features, alpha)"
  
      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
      """
      return make_sparse_spd_matrix(
          n_block_features,
          alpha=np.abs(1.0 - alpha),
          smallest_coef=self.spd_low,
          largest_coef=self.spd_high,
          random_state=self.prng,
      )
  
file_path: skggm/inverse_covariance/profiling/erdos_renyi_graph.py
imports: from __future__ import absolute_import
  import numpy
  from sklearn.datasets import make_sparse_spd_matrix
  from .graphs import Graph
label_desc: Use image node_id 2 for calling the ErdosRenyiGraph obj's underlying member method code with example usage: obj.prototype_adjacency(n_block_features, alpha) and returns: make_sparse_spd_matrix