class_docstr: Base class for algebraic representation of Structural Equation Models(SEMs). The model is
  represented using the Reticular Action Model (RAM).
class_docstr_tok: ["Base","class","for","algebraic","representation","of","Structural","Equation","Models","(","SEMs",")",".","The","model","is","represented","using","the","Reticular","Action","Model","(","RAM",")","."]
docstr: Generates random samples from the model.
  
  Parameters
  ----------
  n_samples: int
      The number of samples to generate.
  
  Returns
  -------
  pd.DataFrame: The generated samples.
docstr_tok: ["Generates","random","samples","from","the","model",".","Parameters","--","--","--","--","--","n_samples",":","int","The","number","of","samples","to","generate",".","Returns","--","--","--","-","pd.DataFrame",":","The","generated","samples","."]
code: def generate_samples(self, n_samples=100):
      """
      Generates random samples from the model.
  
      Parameters
      ----------
      n_samples: int
          The number of samples to generate.
  
      Returns
      -------
      pd.DataFrame: The generated samples.
      """
      if (self.B_fixed_mask is None) or (self.zeta_fixed_mask is None):
          raise ValueError(
              "Parameters for the model has not been specified."
          )
  
      B_inv = np.linalg.inv(
          np.eye(self.B_fixed_mask.shape[0]) - self.B_fixed_mask
      )
      implied_cov = (
          self.wedge_y
          @ B_inv
          @ self.zeta_fixed_mask
          @ B_inv.T
          @ self.wedge_y.T
      )
  
      # Check if implied covariance matrix is positive definite.
      if not np.all(np.linalg.eigvals(implied_cov) > 0):
          raise ValueError(
              "The implied covariance matrix is not positive definite."
              + "Please check model parameters."
          )
  
      # Get the order of observed variables
      x_index, y_index = np.nonzero(self.wedge_y)
      observed = [self.eta[i] for i in y_index]
  
      # Generate samples and return a dataframe.
      samples = np.random.multivariate_normal(
          mean=[0 for i in range(implied_cov.shape[0])],
          cov=implied_cov,
          size=n_samples,
      )
      return pd.DataFrame(samples, columns=observed)
  
imports: import itertools
  import networkx
  import numpy
  import pandas
  from networkx.algorithms.dag import descendants
  from pyparsing import OneOrMore, Optional, Suppress, Word, alphanums, nums
  from pgmpy.base import DAG
  from pgmpy.global_vars import logger
label_desc: Use image node_id 4 for calling the SEMAlg obj's underlying member method code with example usage: obj.generate_samples(n_samples) and returns: pd