diff --git a/spike_sorting.m b/spike_sorting.m
index 95429d9..8ebd965 100644
--- a/spike_sorting.m
+++ b/spike_sorting.m
@@ -79,7 +79,7 @@ for fileIndex=1:8
     
     % finds dataset's theshold factor k that produces the closest number of
     % spikes ot the ground truth
-    [minValue, closestIndex] = min(abs(numberOfSpikesTrimmed-Dataset.spikeNum));
+    [~, closestIndex] = min(abs(numberOfSpikesPerFactor-Dataset.spikeNum));
     datasetFactors(fileIndex) = thresholdFactorInitValue + (closestIndex - 1) * thresholdFactorStep;
     
     clear Dataset
@@ -186,6 +186,7 @@ for fileIndex=1:4
     title(['Spikes of dataset #' num2str(fileIndex) ' aligned at the first extrema']);
     xlabel('Samples');
     ylabel('Trivial Unit');
+    xlim([1 64]);
     drawnow;
     hold off;
     
@@ -275,10 +276,22 @@ for fileIndex=1:4
     end
     
     [~, dbScanClasses, ~] = dbscan(features(:, 6:7)', distThreshold, minPts);
+    %[dbScanClasses, accuracy] = ShuffledClassAccuracy(spikeClass, dbScanClasses);
     % fixes classes enumeration
-    dbScanClasses(dbScanClasses==1) = 7;
-    dbScanClasses(dbScanClasses==3) = 1;
-    dbScanClasses(dbScanClasses==7) = 3;
+    if fileIndex == 1
+        dbScanClasses(dbScanClasses==1) = 7;
+        dbScanClasses(dbScanClasses==3) = 1;
+        dbScanClasses(dbScanClasses==7) = 3;
+    elseif fileIndex == 3
+        dbScanClasses(dbScanClasses==1) = 7;
+        dbScanClasses(dbScanClasses==2) = 1;
+        dbScanClasses(dbScanClasses==7) = 2;
+    elseif fileIndex == 4
+        dbScanClasses(dbScanClasses==1) = 7;
+        dbScanClasses(dbScanClasses==3) = 1;
+        dbScanClasses(dbScanClasses==2) = 3;
+        dbScanClasses(dbScanClasses==7) = 2;
+    end
     
     figure();
     scatter(features(dbScanClasses == 0, 6), features(dbScanClasses == 0, 7), [], 'k', 'o');
@@ -297,9 +310,15 @@ for fileIndex=1:4
     linkages = linkage(distances, 'ward');
     hierarchicalClusters = cluster(linkages, 'maxclust', 3);
     % fixes classes enumeration
-    hierarchicalClusters(hierarchicalClusters==1) = 7;
-    hierarchicalClusters(hierarchicalClusters==2) = 1;
-    hierarchicalClusters(hierarchicalClusters==7) = 2;
+    if fileIndex == 1
+        hierarchicalClusters(hierarchicalClusters==1) = 7;
+        hierarchicalClusters(hierarchicalClusters==2) = 1;
+        hierarchicalClusters(hierarchicalClusters==7) = 2;
+    elseif fileIndex == 3
+        hierarchicalClusters(hierarchicalClusters==1) = 7;
+        hierarchicalClusters(hierarchicalClusters==2) = 1;
+        hierarchicalClusters(hierarchicalClusters==7) = 2;
+    end
     
     figure();
     scatter(features(hierarchicalClusters == 0, 6), features(hierarchicalClusters == 0, 7), [], 'k', 'o');
@@ -317,9 +336,20 @@ for fileIndex=1:4
     rng(1); % For reproducibility
     kMeansClasses = kmeans(features(:, 6:7), 3);
     % fixes classes enumeration
-    kMeansClasses(kMeansClasses==2) = 7;
-    kMeansClasses(kMeansClasses==3) = 2;
-    kMeansClasses(kMeansClasses==7) = 3;
+    if fileIndex == 1
+        kMeansClasses(kMeansClasses==2) = 7;
+        kMeansClasses(kMeansClasses==3) = 2;
+        kMeansClasses(kMeansClasses==7) = 3;
+    elseif fileIndex == 2
+        kMeansClasses(kMeansClasses==1) = 7;
+        kMeansClasses(kMeansClasses==2) = 1;
+        kMeansClasses(kMeansClasses==7) = 2;
+    elseif fileIndex == 3
+        kMeansClasses(kMeansClasses==1) = 7;
+        kMeansClasses(kMeansClasses==2) = 1;
+        kMeansClasses(kMeansClasses==3) = 2;
+        kMeansClasses(kMeansClasses==7) = 3;
+    end
     
     figure();
     scatter(features(kMeansClasses == 1, 6), features(kMeansClasses == 1, 7), [], 'b', '*');