parameters: (self, X)
docstring: Check the parameters of the Gaussian distribution.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
docstring_summary: Check the parameters of the Gaussian distribution.
docstring_tokens: Check the parameters of the Gaussian distribution .
function: def _check_means_parameters(self, X):
          """Check the parameters of the Gaussian distribution.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
          """
          _, n_features = X.shape
  
          if self.mean_precision_prior is None:
              self.mean_precision_prior_ = 1.
          elif self.mean_precision_prior > 0.:
              self.mean_precision_prior_ = self.mean_precision_prior
          else:
              raise ValueError("The parameter 'mean_precision_prior' should be "
                               "greater than 0., but got %.3f."
                               % self.mean_precision_prior)
  
          if self.mean_prior is None:
              self.mean_prior_ = X.mean(axis=0)
          else:
              self.mean_prior_ = check_array(self.mean_prior,
                                             dtype=[np.float64, np.float32],
                                             ensure_2d=False)
              _check_shape(self.mean_prior_, (n_features, ), 'means')
function_tokens: def _check_means_parameters ( self , X ) : _ , n_features = X . shape if self . mean_precision_prior is None : self . mean_precision_prior_ = 1. elif self . mean_precision_prior > 0. : self . mean_precision_prior_ = self . mean_precision_prior else : raise ValueError ( "The parameter 'mean_precision_prior' should be " "greater than 0., but got %.3f." % self . mean_precision_prior ) if self . mean_prior is None : self . mean_prior_ = X . mean ( axis = 0 ) else : self . mean_prior_ = check_array ( self . mean_prior , dtype = [ np . float64 , np . float32 ] , ensure_2d = False ) _check_shape ( self . mean_prior_ , ( n_features , ) , 'means' )