code: def f_comb(self, pos, sample):
          
          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
docs: 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