clear all;

subject = [];

accuracy = [];

for k = 2:6

log_dir = 'E:\Classification\Generated from Logfile(base)\Male\recreated_vrp\';

vrp_dir = 'E:\Classification\generated from VRP\Male\recreated_vrp\';

vrp_folder = dir(vrp_dir);

for i = 1:length(vrp_folder)

% Remove system folders.

if(isequal(vrp_folder(i).name,'.')||...

isequal(vrp_folder(i).name,'..')||...

~vrp_folder(i).isdir)

continue

end

subject_name = vrp_folder(i).name;

log_2_dir = fullfile(log_dir,subject_name);

vrp_2_dir = fullfile(vrp_dir,subject_name);

k_string = ['k=',char(string((k)))];

log_2_folder = dir(log_2_dir);

vrp_2_folder = dir(vrp_2_dir);

subject = [subject; [subject_name, '_',char(string(k))]];

for c = 1:length(log_2_folder)

if contains(log_2_folder(c).name, k_string)

[~, vrpArray_log] = FonaDynLoadVRP(fullfile(log_2_dir,log_2_folder(c).name));

end

end

[vrpArray_log, ~] = setClustersPos(vrpArray_log, vrpArray_log(:,11), k);

for c = 1:length(vrp_2_folder)

if contains(vrp_2_folder(c).name, k_string)

[~, vrpArray_vrp] = FonaDynLoadVRP(fullfile(vrp_2_dir,vrp_2_folder(c).name));

end

end

[vrpArray_vrp, ~] = setClustersPos_copy(vrpArray_vrp, vrpArray_vrp(:,10), k);

accuracy_count = 0;

[a,b,c] = intersect(vrpArray_log(:,1:2), vrpArray_vrp(:,1:2),'rows');

for j = 1:length(b)

if vrpArray_log(b(j),11) == vrpArray_vrp(c(j), 10)

accuracy_count = accuracy_count+1;

end

end

accuracy = [accuracy; accuracy_count / size(a,1)];

end

function [trained, Dic] = setClustersPos(data, idx, k)

meanSPL = [];

meanF0 = [];

trained = data;

for i = 1:k

meanSPL(i) = mean(data(idx == i, 2));

meanF0(i) = mean(data(idx == i,1));

end

[order1, Dic1] = sort(meanSPL);

[order2, Dic2] = sort(meanF0);

meansquare = Dic1 .* Dic2;

[order, Dic] = sort(meansquare);

for ii = 1:k

for j = 1:k

if meansquare(j) == order(ii)

trained(idx==j, 11) = ii;

end

end

end

function [trained, Dic] = setClustersPos_copy(data, idx, k)

meanSPL = [];

meanF0 = [];

trained = data;

for i = 1:k

meanSPL(i) = mean(data(idx == i, 2));

meanF0(i) = mean(data(idx == i,1));

end

[order1, Dic1] = sort(meanSPL);

[order2, Dic2] = sort(meanF0);

meansquare = Dic1 .* Dic2;

[order, Dic] = sort(meansquare);

for ii = 1:k

for j = 1:k

if meansquare(j) == order(ii)

trained(idx==j, 10) = ii;

end

end

end