function [TestPCAOutput, distMatrix] = computekNNClassificationPerformanceTrainTest(ftrain_all, ftestPCA_all, TrnPCALabels, TestPCALabels, sizeTest, stepPrint, numCoresKnn, param)

%Distance matrix

distMatrix = full(fastEuclideanDistance(ftestPCA_all, ftrain_all));

% whos ftrain_all ftestPCA_all distMatrix

%modo parallelo per knn (k = 1) classification

%leave-one-out

%we use knn_neighbors + 1 because otherwise it would find the same vector

%in this way we choose the second neighbor (which is the actual first neighbor)

%(we use the entire feature vector for all samples)

%loop on test samples

%init

TestPCAOutput = zeros(sizeTest, 1);

% start_pool(numCoresKnn);

% parfor g = 1 : sizeTest

for g = 1 : sizeTest

    %get id of current worker

    %t = getCurrentTask();

    %display progress

    if mod(g, stepPrint) == 0

        %fprintf(1, ['\t\tCore ' num2str(t.ID) ': ' num2str(g) ' / ' num2str(sizeTest) '\n'])

        fprintf(1, ['\t\t' num2str(g) ' / ' num2str(sizeTest) '\n'])

    end %if mod(i, 100) == 0

    %we can re-use the distance matrix

    distV = distMatrix(g, :);

    sortV = sort(distV, 'ascend');

    %minD = sortV(2); %the first will be 0

    minD = sortV(1); %the first will be 0

    idx = find(distV == minD);

    idx = idx(1); %se dovessero essercene altri a pari merito

    TestPCAOutput(g) = idx;

end %for g

%mettiamo le labels al posto degli indici trovati

for g = 1 : sizeTest

    %TestPCAOutput(g) = TestPCALabels(TestPCAOutput(g));

    TestPCAOutput(g) = TrnPCALabels(TestPCAOutput(g));

end %for g