code: def alpha2_calc(
      TOP, P, ACC, POP, classes, max_iter=200, epsilon=0.0001
  ):
      """
      Calculate Aickin's alpha.
  
      :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 ACC: accuracy
      :type ACC: float
      :param POP: population or total number of samples per class
      :type POP: dict
      :param classes: confusion matrix classes
      :type classes: list
      :param max_iter: maximum iteration
      :type max_iter: int
      :param epsilon: difference threshold
      :type epsilon: float
      :return: Aickin's alpha as float
      """
      try:
          p_A = {i: TOP[i] / POP[i] for i in classes}
          p_B = {i: P[i] / POP[i] for i in classes}
          step = 1
          alpha = 0
          alpha_prev = 0
          while True:
              p_e = 0
              for i in classes:
                  p_e += p_A[i] * p_B[i]
              alpha_prev = alpha
              alpha = reliability_calc(p_e, ACC)
              for i in classes:
                  p_A[i] = TOP[i] / (
                      ((1 - alpha) + alpha * p_B[i] / p_e) * POP[i]
                  )
                  p_B[i] = P[i] / (
                      ((1 - alpha) + alpha * p_A[i] / p_e) * POP[i]
                  )
              if step > max_iter or abs(alpha - alpha_prev) < epsilon:
                  break
              step += 1
          return alpha
      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 3 for calling a global function with example usage: alpha2_calc(TOP, P, ACC, POP, classes, max_iter, epsilon) and returns: alpha, str