--- a
+++ b/functions/functions_Classifiers/computekNNClassificationPerformance.m
@@ -0,0 +1,68 @@
+function [TestPCAOutput, distMatrix] = computekNNClassificationPerformance(ftestPCA_all, TestPCALabels, sizeTest, stepPrint, numCoresKnn, param)
+
+
+%Distance matrix
+if strcmp(param.matchDistance, 'chisq')
+    distMatrix = sc_pdist2(ftestPCA_all, ftestPCA_all, 'chisq');
+elseif strcmp(param.matchDistance, 'euclidean')
+    %fast Euclidean distance
+    distMatrix = full(fastEuclideanDistance(ftestPCA_all, ftestPCA_all));
+else %if strcmp
+    distMatrix = pdist2(ftestPCA_all', ftestPCA_all', param.matchDistance);
+end %if strcmp
+
+%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);
+
+%parallel vars init
+% ftestPCA_all = parallel.pool.Constant(ftestPCA_all);
+knnDistancePar = param.knnDistance;
+matchDistance = param.matchDistance;
+numkPar = param.knn_neighbors;
+
+% 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
+    
+    if strcmp(knnDistancePar, matchDistance)
+        %we can re-use the distance matrix
+        distV = distMatrix(g, :);
+        sortV = sort(distV, 'ascend');
+        minD = sortV(2); %the first will be 0
+        idx = find(distV == minD);
+        idx = idx(1); %se dovessero essercene altri a pari merito  
+        
+    else %if strcmp(param.knnDistance, param.matchDistance)
+        %we use + 1
+        %idx = knnsearch(ftestPCA_all.Value', ftestPCA_all.Value(:, g)', 'K', numkPar + 1, 'Distance', knnDistancePar);
+        idx = knnsearch(ftestPCA_all', ftestPCA_all(:, g)', 'K', numkPar + 1, 'Distance', knnDistancePar);
+        idx(idx == g)  = []; %il pių vicino č il vettore stesso, lo togliamo
+        idx = idx(1); %se dovessero essercene altri a pari merito
+    end %if strcmp(param.knnDistance, param.matchDistance)
+    
+    TestPCAOutput(g) = idx;
+    
+end %for g
+
+%mettiamo le labels al posto degli indici trovati
+for g = 1 : sizeTest
+    TestPCAOutput(g) = TestPCALabels(TestPCAOutput(g));
+end %for g
+
+