Change DB-SCAN parameters per dataset

spike_sorting.m

@@ -118,7 +118,7 @@ for fileIndex=1:4
     %% Q.2.1 and Q.2.2
     dataMedian = median(abs(data)/0.6745);
     
-    factorEstimation = polyval(empiricalRule, dataMedian);
+    %factorEstimation = polyval(empiricalRule, dataMedian);
     factorEstimation = 4;
     threshold = factorEstimation * dataMedian;
     numberOfSpikes = 0;
@@ -260,7 +260,21 @@ for fileIndex=1:4
     % clustering using DB-SCAN algorithm
     % code for DB-SCAN downloaded from here:
     % https://www.peterkovesi.com/matlabfns/
-    [~, dbScanClasses, ~] = dbscan(features(:, 6:7)', 0.4, 20);
+    if fileIndex == 1
+        distThreshold = 0.4;
+        minPts = 20;
+    elseif fileIndex == 2
+        distThreshold = 0.15;
+        minPts = 30;
+    elseif fileIndex == 3
+        distThreshold = 0.32;
+        minPts = 30;
+    else
+        distThreshold = 0.36;
+        minPts = 35;
+    end
+    
+    [~, dbScanClasses, ~] = dbscan(features(:, 6:7)', distThreshold, minPts);
     % fixes classes enumeration
     dbScanClasses(dbScanClasses==1) = 7;
     dbScanClasses(dbScanClasses==3) = 1;
@@ -293,12 +307,11 @@ for fileIndex=1:4
     scatter(features(hierarchicalClusters == 2, 6), features(hierarchicalClusters == 2, 7), [], 'r', '*');
     scatter(features(hierarchicalClusters == 3, 6), features(hierarchicalClusters == 3, 7), [], 'g', '*');
     scatter(features(hierarchicalClusters == 1, 6), features(hierarchicalClusters == 1, 7), [], 'b', '*');
-    title(['Dataset #' num2str(fileIndex) ' feature plot after clustering with K-Means']);
+    title(['Dataset #' num2str(fileIndex) ' feature plot after clustering with hierarchical clustering']);
     xlabel('PCA feature 1');
     ylabel('PCA feature 2');
     accuracy = classperf(spikeClass',hierarchicalClusters);
-    fprintf('Accuracy achieved with K-Means is %.2f%%\n\n', accuracy.CorrectRate*100);
+    fprintf('Accuracy achieved with hierarchical clustering is %.2f%%\n\n', accuracy.CorrectRate*100);
-    
     
     % clustering using kmeans algorithm
     rng(1); % For reproducibility