parameters: (self, X, y=None)
return_statement: return self
docstring: Fits the Maximum Likelihood Estimator covariance model
          according to the given training data and parameters.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
            Training data, where n_samples is the number of samples and
            n_features is the number of features.
  
          y
              not used, present for API consistence purpose.
  
          Returns
          -------
          self : object
docstring_summary: Fits the Maximum Likelihood Estimator covariance model
          according to the given training data and parameters.
docstring_tokens: Fits the Maximum Likelihood Estimator covariance model according to the given training data and parameters .
function: def fit(self, X, y=None):
          """Fits the Maximum Likelihood Estimator covariance model
          according to the given training data and parameters.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
            Training data, where n_samples is the number of samples and
            n_features is the number of features.
  
          y
              not used, present for API consistence purpose.
  
          Returns
          -------
          self : object
  
          """
          X = check_array(X)
          if self.assume_centered:
              self.location_ = np.zeros(X.shape[1])
          else:
              self.location_ = X.mean(0)
          covariance = empirical_covariance(
              X, assume_centered=self.assume_centered)
          self._set_covariance(covariance)
  
          return self
function_tokens: def fit ( self , X , y = None ) : X = check_array ( X ) if self . assume_centered : self . location_ = np . zeros ( X . shape [ 1 ] ) else : self . location_ = X . mean ( 0 ) covariance = empirical_covariance ( X , assume_centered = self . assume_centered ) self . _set_covariance ( covariance ) return self