'Fit the model using X, y as training data.
Parameters
X : array-like, shape (n_samples,)
Training data.
y : array-like, shape (n_samples,)
Training target.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Returns
self : object
Returns an instance of self.'

def fit(self, X, y, sample_weight=None):

    X = column_or_1d(X)
    y = column_or_1d(y)
    (X, y) = indexable(X, y)
    (self.a_, self.b_) = _sigmoid_calibration(X, y, sample_weight)
    return self
