parameters: (self, X)
docstring: Check the `covariance_prior_`.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
docstring_summary: Check the `covariance_prior_`.
docstring_tokens: Check the covariance_prior_ .
function: def _checkcovariance_prior_parameter(self, X):
          """Check the `covariance_prior_`.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
          """
          _, n_features = X.shape
  
          if self.covariance_prior is None:
              self.covariance_prior_ = {
                  'full': np.atleast_2d(np.cov(X.T)),
                  'tied': np.atleast_2d(np.cov(X.T)),
                  'diag': np.var(X, axis=0, ddof=1),
                  'spherical': np.var(X, axis=0, ddof=1).mean()
              }[self.covariance_type]
  
          elif self.covariance_type in ['full', 'tied']:
              self.covariance_prior_ = check_array(
                  self.covariance_prior, dtype=[np.float64, np.float32],
                  ensure_2d=False)
              _check_shape(self.covariance_prior_, (n_features, n_features),
                           '%s covariance_prior' % self.covariance_type)
              _check_precision_matrix(self.covariance_prior_,
                                      self.covariance_type)
          elif self.covariance_type == 'diag':
              self.covariance_prior_ = check_array(
                  self.covariance_prior, dtype=[np.float64, np.float32],
                  ensure_2d=False)
              _check_shape(self.covariance_prior_, (n_features,),
                           '%s covariance_prior' % self.covariance_type)
              _check_precision_positivity(self.covariance_prior_,
                                          self.covariance_type)
          # spherical case
          elif self.covariance_prior > 0.:
              self.covariance_prior_ = self.covariance_prior
          else:
              raise ValueError("The parameter 'spherical covariance_prior' "
                               "should be greater than 0., but got %.3f."
                               % self.covariance_prior)
function_tokens: def _checkcovariance_prior_parameter ( self , X ) : _ , n_features = X . shape if self . covariance_prior is None : self . covariance_prior_ = { 'full' : np . atleast_2d ( np . cov ( X . T ) ) , 'tied' : np . atleast_2d ( np . cov ( X . T ) ) , 'diag' : np . var ( X , axis = 0 , ddof = 1 ) , 'spherical' : np . var ( X , axis = 0 , ddof = 1 ) . mean ( ) } [ self . covariance_type ] elif self . covariance_type in [ 'full' , 'tied' ] : self . covariance_prior_ = check_array ( self . covariance_prior , dtype = [ np . float64 , np . float32 ] , ensure_2d = False ) _check_shape ( self . covariance_prior_ , ( n_features , n_features ) , '%s covariance_prior' % self . covariance_type ) _check_precision_matrix ( self . covariance_prior_ , self . covariance_type ) elif self . covariance_type == 'diag' : self . covariance_prior_ = check_array ( self . covariance_prior , dtype = [ np . float64 , np . float32 ] , ensure_2d = False ) _check_shape ( self . covariance_prior_ , ( n_features , ) , '%s covariance_prior' % self . covariance_type ) _check_precision_positivity ( self . covariance_prior_ , self . covariance_type ) # spherical case elif self . covariance_prior > 0. : self . covariance_prior_ = self . covariance_prior else : raise ValueError ( "The parameter 'spherical covariance_prior' " "should be greater than 0., but got %.3f." % self . covariance_prior )