parameters: (self, X, y, sample_weight=None)
return_statement: return self._partial_fit(X, y, self.alpha, C=1.0,
                                   loss=self.loss,
                                   learning_rate=self.learning_rate, n_iter=1,
                                   sample_weight=sample_weight,
                                   coef_init=None, intercept_init=None)
docstring: Fit linear model with Stochastic Gradient Descent.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Subset of training data
  
          y : numpy array of shape (n_samples,)
              Subset of target values
  
          sample_weight : array-like, shape (n_samples,), optional
              Weights applied to individual samples.
              If not provided, uniform weights are assumed.
  
          Returns
          -------
          self : returns an instance of self.
docstring_summary: Fit linear model with Stochastic Gradient Descent.
docstring_tokens: Fit linear model with Stochastic Gradient Descent .
function: def partial_fit(self, X, y, sample_weight=None):
          """Fit linear model with Stochastic Gradient Descent.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Subset of training data
  
          y : numpy array of shape (n_samples,)
              Subset of target values
  
          sample_weight : array-like, shape (n_samples,), optional
              Weights applied to individual samples.
              If not provided, uniform weights are assumed.
  
          Returns
          -------
          self : returns an instance of self.
          """
          return self._partial_fit(X, y, self.alpha, C=1.0,
                                   loss=self.loss,
                                   learning_rate=self.learning_rate, n_iter=1,
                                   sample_weight=sample_weight,
                                   coef_init=None, intercept_init=None)
function_tokens: def partial_fit ( self , X , y , sample_weight = None ) : return self . _partial_fit ( X , y , self . alpha , C = 1.0 , loss = self . loss , learning_rate = self . learning_rate , n_iter = 1 , sample_weight = sample_weight , coef_init = None , intercept_init = None )