code: def convex_combination(
      classes, TP, TOP, P, class_name, modified=False
  ):
      """
      Calculate Overall_CEN coefficient.
  
      :param classes: confusion matrix classes
      :type classes: list
      :param TP: true positive
      :type TP: dict
      :param TOP: number of positives in predict vector per class
      :type TOP: dict
      :param P: number of actual positives per class
      :type P: dict
      :param class_name: reviewed class name
      :type class_name: any valid type
      :param modified: modified mode flag
      :type modified: bool
      :return: Overall_CEN coefficient as float
      """
      try:
          class_number = len(classes)
          alpha = 1
          if class_number == 2:
              alpha = 0
          matrix_sum = sum(list(TOP.values()))
          TP_sum = sum(list(TP.values()))
          up = TOP[class_name] + P[class_name]
          down = 2 * matrix_sum
          if modified:
              down -= alpha * TP_sum
              up -= TP[class_name]
          return up / down
      except Exception:
          return "None"
  
imports: from __future__ import division
  import math
  import operator
  from functools import reduce
  from .pycm_interpret import *
  from .pycm_ci import kappa_SE_calc, CI_calc, SE_calc
  from .pycm_util import complement
label_desc: Use image node_id 14 for calling a global function with example usage: convex_combination(classes, TP, TOP, P, class_name, modified) and returns: unknown, str