function [co] = DsymBregman_DBregman_initialization(mult)
%Initialization of the symmetric Bregman distance estimator using the relation: D_S = (D_NS(f1,f2) + D_NS (f2,f1)) / alpha, where D_NS is the nonsymmetric Bregman distance.
%
%Note:
% 1)The estimator is treated as a cost object (co).
% 2)We use the naming convention 'D_initialization' to ease embedding new divergence estimation methods.
% 3)This is a meta method: the (nonsymmetric) Bregman distance estimator can arbitrary.
%
%INPUT:
% mult: is a multiplicative constant relevant (needed) in the estimation; '=1' means yes, '=0' no.
%OUTPUT:
% co: cost object (structure).
%
%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
%
%This file is part of the ITE (Information Theoretical Estimators) Matlab/Octave toolbox.
%
%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
%
%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
%
%You should have received a copy of the GNU General Public License along with ITE. If not, see .
%mandatory fields:
co.name = 'symBregman_DBregman';
co.mult = mult;
%other fields:
co.alpha = 0.7; %assumption: not equal to 1
co.member_name = 'Bregman_kNN_k'; %you can change it to any (nonsymmetric) Bregman distance estimator
co.member_co = D_initialization(co.member_name,mult);
co.member_co.alpha = co.alpha; %automatism for setting the parameters (co.alpha) of member_co (co_2) in a <=2 deep meta construction (co_1 -> co_2); otherwise, please set the parameters (co.alpha) in the member_co-s.