parameters: (degrees_of_freedom, log_det_precisions_chol, n_features)
return_statement: return -(degrees_of_freedom * log_det_precisions_chol +
               degrees_of_freedom * n_features * .5 * math.log(2.) +
               np.sum(gammaln(.5 * (degrees_of_freedom -
                                    np.arange(n_features)[:, np.newaxis])), 0))
docstring: Compute the log of the Wishart distribution normalization term.
  
      Parameters
      ----------
      degrees_of_freedom : array-like, shape (n_components,)
          The number of degrees of freedom on the covariance Wishart
          distributions.
  
      log_det_precision_chol : array-like, shape (n_components,)
           The determinant of the precision matrix for each component.
  
      n_features : int
          The number of features.
  
      Return
      ------
      log_wishart_norm : array-like, shape (n_components,)
          The log normalization of the Wishart distribution.
docstring_summary: Compute the log of the Wishart distribution normalization term.
docstring_tokens: Compute the log of the Wishart distribution normalization term .
function: def _log_wishart_norm(degrees_of_freedom, log_det_precisions_chol, n_features):
      """Compute the log of the Wishart distribution normalization term.
  
      Parameters
      ----------
      degrees_of_freedom : array-like, shape (n_components,)
          The number of degrees of freedom on the covariance Wishart
          distributions.
  
      log_det_precision_chol : array-like, shape (n_components,)
           The determinant of the precision matrix for each component.
  
      n_features : int
          The number of features.
  
      Return
      ------
      log_wishart_norm : array-like, shape (n_components,)
          The log normalization of the Wishart distribution.
      """
      # To simplify the computation we have removed the np.log(np.pi) term
      return -(degrees_of_freedom * log_det_precisions_chol +
               degrees_of_freedom * n_features * .5 * math.log(2.) +
               np.sum(gammaln(.5 * (degrees_of_freedom -
                                    np.arange(n_features)[:, np.newaxis])), 0))
function_tokens: def _log_wishart_norm ( degrees_of_freedom , log_det_precisions_chol , n_features ) : # To simplify the computation we have removed the np.log(np.pi) term return - ( degrees_of_freedom * log_det_precisions_chol + degrees_of_freedom * n_features * .5 * math . log ( 2. ) + np . sum ( gammaln ( .5 * ( degrees_of_freedom - np . arange ( n_features ) [ : , np . newaxis ] ) ) , 0 ) )