'Fit linear model with Passive Aggressive algorithm.
Parameters
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : numpy array of 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.
Returns
self : returns an instance of self.'

def fit(self, X, y, coef_init=None, intercept_init=None):

    lr = ('pa1' if (self.loss == 'epsilon_insensitive') else 'pa2')
    return self._fit(X, y, alpha=1.0, C=self.C, loss='epsilon_insensitive', learning_rate=lr, coef_init=coef_init, intercept_init=intercept_init)
