code: def normalize_weight(self, samples):
          
          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
docs: 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