code: def sigma_sq(self, sample):
          
          ret = 0
          for i in range(1, self.point_num + 1):
              temp = self.trial_history[i - 1] - self.f_comb(i, sample)
              ret += temp * temp
          return 1.0 * ret / self.point_num
docs: returns the value of sigma square, given the weight's sample
          
          Parameters
          ----------
          sample: list
              sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
  
          Returns
          -------
          float
              the value of sigma square, given the weight's sample