class_docstr: Confusion matrix class.
  
  >>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
  >>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
  >>> cm = ConfusionMatrix(y_actu, y_pred)
  >>> cm.classes
  [0, 1, 2]
  >>> cm.table
  {0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}
  >>> cm2 = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2": 2}, "Class2": {"Class1": 0, "Class2": 5}})
  >>> cm2
  pycm.ConfusionMatrix(classes: ['Class1', 'Class2'])
class_docstr_tok: ["Confusion","matrix","class",".",">",">",">","y_actu","=","[","2",",","0",",","2",",","2",",","0",",","1",",","1",",","2",",","2",",","0",",","1",",","2","]",">",">",">","y_pred","=","[","0",",","0",",","2",",","1",",","0",",","2",",","1",",","0",",","2",",","0",",","2",",","2","]",">",">",">","cm","=","ConfusionMatrix","(","y_actu",",","y_pred",")",">",">",">","cm.classes","[","0",",","1",",","2","]",">",">",">","cm.table","{","0",":","{","0",":","3",",","1",":","0",",","2",":","0","}",",","1",":","{","0",":","0",",","1",":","1",",","2",":","2","}",",","2",":","{","0",":","2",",","1",":","1",",","2",":","3","}","}",">",">",">","cm2","=","ConfusionMatrix","(","matrix=","{","``","Class1","''",":","{","``","Class1","''",":","1",",","``","Class2","''",":","2","}",",","``","Class2","''",":","{","``","Class1","''",":","0",",","``","Class2","''",":","5","}","}",")",">",">",">","cm2","pycm.ConfusionMatrix","(","classes",":","[","'Class1","'",",","'Class2","'","]",")"]
docstr: Calculate the average of the input parameter.
  
  :param param: input parameter
  :type param: str
  :param none_omit: none items omitting flag
  :type none_omit: bool
  :return: average of the input parameter
code: def average(self, param, none_omit=False):
      """
      Calculate the average of the input parameter.
  
      :param param: input parameter
      :type param: str
      :param none_omit: none items omitting flag
      :type none_omit: bool
      :return: average of the input parameter
      """
      return self.weighted_average(
          param=param, weight=self.POP, none_omit=none_omit
      )
  
imports: from __future__ import division
  from .pycm_error import pycmVectorError, pycmMatrixError, pycmCIError, pycmAverageError, pycmPlotError
  from .pycm_handler import __class_stat_init__, __overall_stat_init__
  from .pycm_handler import __obj_assign_handler__, __obj_file_handler__, __obj_matrix_handler__, __obj_vector_handler__, __obj_array_handler__
  from .pycm_handler import __imbalancement_handler__
  from .pycm_class_func import F_calc, IBA_calc, TI_calc, NB_calc, sensitivity_index_calc
  from .pycm_overall_func import weighted_kappa_calc, weighted_alpha_calc, alpha2_calc, brier_score_calc, log_loss_calc
  from .pycm_distance import DistanceType, DISTANCE_MAPPER
  from .pycm_output import *
  from .pycm_util import *
  from .pycm_param import *
  from .pycm_ci import __CI_overall_handler__, __CI_class_handler__
  import os
  import json
  import numpy
  from warnings import warn
label_desc: Use image node_id 27 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.average(param, none_omit) and returns: self