function [loss] = myMMRE(actuals,predictions,algorithms,preprocessors,datasetName)

% MRE = abs(actual - predicted)/actual and MMRE is the mean of all MREs

% define a variable to keep the MMRE results for each algorithm
MREResults = -1 * ones(size(algorithms,1),size(actuals,1));

% below two variables are used for plotting purposes later in the algorithm
minSumMRE = inf;    % first define miminum sum of MRE as plus infinity
minSumMREIndex = 0; % index is the row number

% process each algorithm's results to find the MRE values
for i = 1:size(algorithms,1)
   MREResults(i,:) = abs(actuals' - predictions(i,:))./actuals';
   if sum(MREResults(i,:)) < minSumMRE
       minSumMRE = sum(MREResults(i,:));
       minSumMREIndex = i;
   end   
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% this part is for plotting purposes only

% MREResultsToPlot = MREResults;  % copy the MRE results to protect from changes
% % get the indices of the sorted MRE's for the method that had the lowest
% % sum MRE
% [C sortedIndex] = sort(MREResultsToPlot(minSumMREIndex,:));
% % then apply this indexing to all the methods
% toSave = figure('units','normalized','outerposition',[0 0 1 1]);;
% hold all;
% % axis([0 size(predictions,2) 0 10]);
% set(gca,'FontSize', 6,'FontName','Times New Roman');
% title(char(datasetName));
% for i = 1:size(algorithms,1)
%    MREResultsToPlot(i,:) = MREResultsToPlot(i,sortedIndex);  
%    subplot(3,3,i);
%    eval(['h' num2str(i) ' = semilogy(MREResultsToPlot(i,:));']);
%    eval(['set(h' num2str(i) ',  ''LineStyle'', ''-'', ''LineWidth'', 1.0, ''Color'', ''Black'');']);
%    eval(['set(h' num2str(i) ', ''Marker'', ''o'', ''MarkerFaceColor'', [0 0 0], ''MarkerEdgeColor'', [0 0 0], ''MarkerSize'', 2);']);
%    legendStr = [ char(preprocessors(i)) '-' char(algorithms(i))];
%    legend(legendStr);
% end
% hold off;
% saveas(toSave, [char(datasetName) '.png']);
% close(toSave);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% define win tie loss variables
win = zeros(size(algorithms,1),1);
tie = zeros(size(algorithms,1),1);
loss = zeros(size(algorithms,1),1);

% start calculating win tie loss values
for i = 1:size(algorithms,1)
    for j = (i+1):size(algorithms,1)
        if i ~= j   % if i and j are different
            eval(['[P,H] = ranksum(MREResults(',num2str(i),',:),MREResults(',num2str(j),',:));']);
            if H == 0   % if they are the same
                eval(['tie(',num2str(i),') = tie(',num2str(i),') +1;']);
                eval(['tie(',num2str(j),') = tie(',num2str(j),') +1;']);
            else
                % calculate mean values
                eval(['mmrei = mean(MREResults(',num2str(i),',:));']);
                eval(['mmrej = mean(MREResults(',num2str(j),',:));']);
                if mmrei < mmrej
                    eval(['win(',num2str(i),') = win(',num2str(i),') +1;']);
                    eval(['loss(',num2str(j),') = loss(',num2str(j),') +1;']);
                else
                    eval(['win(',num2str(j),') = win(',num2str(j),') +1;']);
                    eval(['loss(',num2str(i),') = loss(',num2str(i),') +1;']);
                end
            end
        end
    end
end

% and now write the loss values to the file
fid=fopen('RUN_RESULTS.txt','a' );
for i = 1:size(algorithms,1)
    fprintf(fid, [num2str(loss(i)) ',']);
end
fprintf(fid, ['\n']);    % append a new line at the end of the line
% close file
fclose(fid);

end