return the value of the f_comb when epoch = pos

        Parameters
        ----------
        pos: int
            the epoch number of the position you want to predict
        sample: list
            sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}

        Returns
        -------
        int
            The expected matrix at pos with all the active function's prediction

def f_comb(self, pos, sample):
        """return the value of the f_comb when epoch = pos

        Parameters
        ----------
        pos: int
            the epoch number of the position you want to predict
        sample: list
            sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}

        Returns
        -------
        int
            The expected matrix at pos with all the active function's prediction
        """
        ret = 0
        for i in range(self.effective_model_num):
            model = self.effective_model[i]
            y = self.predict_y(model, pos)
            ret += sample[i] * y
        return ret