parameters: (self, X, y=None)
return_statement: return self
docstring: Estimate model parameters with the EM algorithm.
  
          A initialization step is performed before entering the
          expectation-maximization (EM) algorithm. If you want to avoid
          this step, set the keyword argument init_params to the empty
          string '' when creating the GMM object. Likewise, if you would
          like just to do an initialization, set n_iter=0.
  
          Parameters
          ----------
          X : array_like, shape (n, n_features)
              List of n_features-dimensional data points.  Each row
              corresponds to a single data point.
  
          Returns
          -------
          self
docstring_summary: Estimate model parameters with the EM algorithm.
docstring_tokens: Estimate model parameters with the EM algorithm .
function: def fit(self, X, y=None):
          """Estimate model parameters with the EM algorithm.
  
          A initialization step is performed before entering the
          expectation-maximization (EM) algorithm. If you want to avoid
          this step, set the keyword argument init_params to the empty
          string '' when creating the GMM object. Likewise, if you would
          like just to do an initialization, set n_iter=0.
  
          Parameters
          ----------
          X : array_like, shape (n, n_features)
              List of n_features-dimensional data points.  Each row
              corresponds to a single data point.
  
          Returns
          -------
          self
          """
          self._fit(X, y)
          return self
function_tokens: def fit ( self , X , y = None ) : self . _fit ( X , y ) return self