function [combining_predicts, errorrate] = combining_NB(DP, test_targets, CM)

% combining method -- Naive bayes
%
% Inputs:
%   DP          -  predicting class by single classifiers
%   test_targets            -  test targets
%   CM                      -  confusion matrices
% Outputs: 
%   combining_predicts      -  combining predicts

% use DP to predict
[nClassifiers, nClasses, nPatterns] = size(DP);

for i = 1: nPatterns
    Support = ones(1, nClasses);
    for j = 1: nClassifiers
        temp = DP(j,:,i);
        [tempmax,ind] = max(temp);
        test_predicts = ind(1) - 1;
        for k = 1: nClasses
            Support(k) = Support(k) * (CM(k,test_predicts + 1,j)/sum(CM(:,test_predicts + 1,j)));
        end
    end
    if sum(CM(:,test_predicts + 1,j)) == 0
        i;
        j;
        k;
    end 
       
       
    [tempmax,ind] = max(Support);
    combining_predicts(i) = ind(1) - 1;
end

errorrate = length(find(combining_predicts ~= test_targets)) / nPatterns;