code: def CEN_calc(classes, table, TOP, P, class_name, modified=False):
      
      try:
          result = 0
          class_number = len(classes)
          for k in classes:
              if k != class_name:
                  P_j_k = CEN_misclassification_calc(
                      table, TOP, P, class_name, k, class_name, modified)
                  P_k_j = CEN_misclassification_calc(
                      table, TOP, P, k, class_name, class_name, modified)
                  if P_j_k != 0:
                      result += P_j_k * math.log(P_j_k, 2 * (class_number - 1))
                  if P_k_j != 0:
                      result += P_k_j * math.log(P_k_j, 2 * (class_number - 1))
          if result != 0:
              result = result * (-1)
          return result
      except Exception:
          return "None"
docs: Calculate CEN (Confusion Entropy)/ MCEN(Modified Confusion Entropy).
  
      :param classes: classes
      :type classes : list
      :param table: input matrix
      :type table : dict
      :param TOP: test outcome positive
      :type TOP : int
      :param P: condition positive
      :type P : int
      :param class_name: reviewed class name
      :type class_name : any valid type
      :param modified : modified mode flag
      :type modified : bool
      :return: CEN(MCEN) as float