normalize weight
        
        Parameters
        ----------
        samples: list
            a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
            representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}

        Returns
        -------
        list
            samples after normalize weight

def normalize_weight(self, samples):
        """normalize weight
        
        Parameters
        ----------
        samples: list
            a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
            representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}

        Returns
        -------
        list
            samples after normalize weight
        """
        for i in range(NUM_OF_INSTANCE):
            total = 0
            for j in range(self.effective_model_num):
                total += samples[i][j]
            for j in range(self.effective_model_num):
                samples[i][j] /= total
        return samples