parameters: (self, X)
return_statement: return (-2 * self.score(X) * X.shape[0] +
                  self._n_parameters() * np.log(X.shape[0]))
docstring: Bayesian information criterion for the current model on the input X.
  
          Parameters
          ----------
          X : array of shape (n_samples, n_dimensions)
  
          Returns
          -------
          bic : float
              The lower the better.
docstring_summary: Bayesian information criterion for the current model on the input X.
docstring_tokens: Bayesian information criterion for the current model on the input X .
function: def bic(self, X):
          """Bayesian information criterion for the current model on the input X.
  
          Parameters
          ----------
          X : array of shape (n_samples, n_dimensions)
  
          Returns
          -------
          bic : float
              The lower the better.
          """
          return (-2 * self.score(X) * X.shape[0] +
                  self._n_parameters() * np.log(X.shape[0]))
function_tokens: def bic ( self , X ) : return ( - 2 * self . score ( X ) * X . shape [ 0 ] + self . _n_parameters ( ) * np . log ( X . shape [ 0 ] ) )