'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_features,)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (1,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
self : returns an instance of self.'

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)
