The linear predictor of the model.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            Test data of which to calculate log-likelihood from

        alpha : float, optional
            Constant that multiplies the penalty terms. If the same alpha was used during training, exact
            coefficients are used, otherwise coefficients are interpolated from the closest alpha values that
            were used during training. If set to ``None``, the last alpha in the solution path is used.

        Returns
        -------
        T : array, shape = (n_samples,)
            The predicted decision function

def predict(self, X, alpha=None):
        """The linear predictor of the model.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            Test data of which to calculate log-likelihood from

        alpha : float, optional
            Constant that multiplies the penalty terms. If the same alpha was used during training, exact
            coefficients are used, otherwise coefficients are interpolated from the closest alpha values that
            were used during training. If set to ``None``, the last alpha in the solution path is used.

        Returns
        -------
        T : array, shape = (n_samples,)
            The predicted decision function
        """
        X = check_array(X)
        coef = self._get_coef(alpha)
        return numpy.dot(X, coef)