parameters: (self, X, y, coef_init=None, intercept_init=None,
              sample_weight=None)
return_statement: return self._fit(X, y, alpha=self.alpha, C=1.0,
                           loss=self.loss, learning_rate=self.learning_rate,
                           coef_init=coef_init, intercept_init=intercept_init,
                           sample_weight=sample_weight)
docstring: Fit linear model with Stochastic Gradient Descent.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Training data
  
          y : numpy array, shape (n_samples,)
              Target values
  
          coef_init : array, shape (n_classes, n_features)
              The initial coefficients to warm-start the optimization.
  
          intercept_init : array, shape (n_classes,)
              The initial intercept to warm-start the optimization.
  
          sample_weight : array-like, shape (n_samples,), optional
              Weights applied to individual samples.
              If not provided, uniform weights are assumed. These weights will
              be multiplied with class_weight (passed through the
              constructor) if class_weight is specified
  
          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 fit(self, X, y, coef_init=None, intercept_init=None,
              sample_weight=None):
          """Fit linear model with Stochastic Gradient Descent.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Training data
  
          y : numpy array, shape (n_samples,)
              Target values
  
          coef_init : array, shape (n_classes, n_features)
              The initial coefficients to warm-start the optimization.
  
          intercept_init : array, shape (n_classes,)
              The initial intercept to warm-start the optimization.
  
          sample_weight : array-like, shape (n_samples,), optional
              Weights applied to individual samples.
              If not provided, uniform weights are assumed. These weights will
              be multiplied with class_weight (passed through the
              constructor) if class_weight is specified
  
          Returns
          -------
          self : returns an instance of self.
          """
          return self._fit(X, y, alpha=self.alpha, C=1.0,
                           loss=self.loss, learning_rate=self.learning_rate,
                           coef_init=coef_init, intercept_init=intercept_init,
                           sample_weight=sample_weight)
function_tokens: def fit ( self , X , y , coef_init = None , intercept_init = None , sample_weight = None ) : return self . _fit ( X , y , alpha = self . alpha , C = 1.0 , loss = self . loss , learning_rate = self . learning_rate , coef_init = coef_init , intercept_init = intercept_init , sample_weight = sample_weight )