Add hierarchical clustering

spike_sorting.m

@@ -39,7 +39,7 @@ for fileIndex=1:8
     dataMedian = median(abs(data)/0.6745);
     datasetMedians(fileIndex) = dataMedian;
     
-    parfor factorIteration=1:numberOfFactors % runs for each k
+    for factorIteration=1:numberOfFactors % runs for each k
         % builds threshold
         thresholdFactor = thresholdFactorInitValue + (factorIteration - 1) * thresholdFactorStep;
         threshold = thresholdFactor * dataMedian;
@@ -258,6 +258,8 @@ for fileIndex=1:4
     fprintf('Accuracy achieved is %.2f%%\n\n', accuracy);
     
     % 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);
     % fixes classes enumeration
     dbScanClasses(dbScanClasses==1) = 7;
@@ -276,6 +278,28 @@ for fileIndex=1:4
     accuracy = classperf(spikeClass',dbScanClasses);
     fprintf('Accuracy achieved with DB-SCAN is %.2f%%\n\n', accuracy.CorrectRate*100);
     
+    % hierarchical clustering
+    distances = pdist(features(:, 6:7));
+    linkages = linkage(distances, 'ward');
+    hierarchicalClusters = cluster(linkages, 'maxclust', 3);
+    % fixes classes enumeration
+    hierarchicalClusters(hierarchicalClusters==1) = 7;
+    hierarchicalClusters(hierarchicalClusters==2) = 1;
+    hierarchicalClusters(hierarchicalClusters==7) = 2;
+    
+    figure();
+    scatter(features(hierarchicalClusters == 0, 6), features(hierarchicalClusters == 0, 7), [], 'k', 'o');
+    hold on;
+    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']);
+    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);
+    
+    
     % clustering using kmeans algorithm
     rng(1); % For reproducibility
     kMeansClasses = kmeans(features(:, 6:7), 3);
@@ -283,12 +307,12 @@ for fileIndex=1:4
     kMeansClasses(kMeansClasses==2) = 7;
     kMeansClasses(kMeansClasses==3) = 2;
     kMeansClasses(kMeansClasses==7) = 3;
+    
     figure();
-    scatter(features(kMeansClasses == 0, 6), features(kMeansClasses == 0, 7), [], 'k', 'o');
+    scatter(features(kMeansClasses == 1, 6), features(kMeansClasses == 1, 7), [], 'b', '*');
     hold on;
     scatter(features(kMeansClasses == 2, 6), features(kMeansClasses == 2, 7), [], 'r', '*');
     scatter(features(kMeansClasses == 3, 6), features(kMeansClasses == 3, 7), [], 'g', '*');
-    scatter(features(kMeansClasses == 1, 6), features(kMeansClasses == 1, 7), [], 'b', '*');
     title(['Dataset #' num2str(fileIndex) ' feature plot after clustering with K-Means']);
     xlabel('PCA feature 1');
     ylabel('PCA feature 2');