diff --git a/MyClassify.m b/MyClassify.m
new file mode 100644
index 0000000..eaa38c5
--- /dev/null
+++ b/MyClassify.m
@@ -0,0 +1,11 @@
+function Acc = MyClassify(Data,group)
+group=categorical(group);
+idx=randperm(size(Data,1),floor(size(Data,1)*0.7));
+train=Data(idx,:);
+trGroup=group(idx);
+test=Data; test(idx,:)=[];
+teGroup=group; teGroup(idx)=[];
+class = classify(test,train,trGroup);
+Acc=(sum(class==teGroup)/numel(teGroup))*100;
+end
+
diff --git a/spike_sorting.m b/spike_sorting.m
index 4cb1468..2d0d04c 100644
--- a/spike_sorting.m
+++ b/spike_sorting.m
@@ -11,9 +11,15 @@
 %%
 %% =================================================================================================
 %% S.1
-datasetMedians = zeros(8);
-datasetFactors = zeros(8);
+datasetMedians(8) = 0;
+datasetFactors(8) = 0;
 
+thresholdFactorInitValue = 3; % k starting value
+thresholdFactorEndValue = 12; % k ending value
+thresholdFactorStep = 0.01; % k jumping step
+numberOfFactors = length(thresholdFactorInitValue:thresholdFactorStep:thresholdFactorEndValue);
+numberOfSpikesPerFactor(numberOfFactors) = 0;
+    
 for fileIndex=1:8
     fprintf('Loading test dataset no. %d\n', fileIndex);
     filename = sprintf('dataset\\Data_Test_%d.mat', fileIndex);
@@ -33,12 +39,6 @@ for fileIndex=1:8
     dataMedian = median(abs(data)/0.6745);
     datasetMedians(fileIndex) = dataMedian;
     
-    thresholdFactorInitValue = 2; % k starting value
-    thresholdFactorEndValue = 14; % k ending value
-    thresholdFactorStep = 0.01; % k jumping step
-    numberOfFactors = length(thresholdFactorInitValue:thresholdFactorStep:thresholdFactorEndValue);
-    numberOfSpikesPerFactor = zeros(numberOfFactors);
-    
     parfor factorIteration=1:numberOfFactors % runs for each k
         % builds threshold
         thresholdFactor = thresholdFactorInitValue + (factorIteration - 1) * thresholdFactorStep;
@@ -82,7 +82,7 @@ for fileIndex=1:8
     [minValue, closestIndex] = min(abs(numberOfSpikesTrimmed-Dataset.spikeNum));
     datasetFactors(fileIndex) = thresholdFactorInitValue + (closestIndex - 1) * thresholdFactorStep;
     
-    clear dataset
+    clear Dataset
     clear data
 end
 fprintf('\n');
@@ -94,7 +94,8 @@ title('Polynomial curve fitting on median-threshold factor value pairs');
 xlabel('Dataset median');
 ylabel('Threshold factor');
 hold on;
-empiricalRule = polyfit(datasetMedians, datasetFactors, 8);
+
+empiricalRule = polyfit(datasetMedians, datasetFactors, 3);
 visualizationX = linspace(0, 0.5, 50);
 visualizationY = polyval(empiricalRule, visualizationX);
 plot(visualizationX, visualizationY);
@@ -137,14 +138,24 @@ for fileIndex=1:4
             while sample <= length(data)
                 sample = sample + 1;
                 if (data(sample) <= threshold)
+                    % finds the index of the max sample for this spike
                     spikeEndIndex = sample;
-                    [~, minIndex] = min(data(spikeStartIndex:spikeEndIndex));
-                    [~, maxIndex] = max(data(spikeStartIndex:spikeEndIndex));
-                    firstIndex = min([minIndex maxIndex]);
+                    [~, relativeMaxIndex] = max(data(spikeStartIndex:spikeEndIndex));
+                    absoluteMaxIndex = spikeStartIndex - 1 + relativeMaxIndex;
+                    
+                    % defines an area of -41/+22 samples around the max
+                    % and searches for the min
+                    [~, relativeMinIndex] = min(data(absoluteMaxIndex-41:absoluteMaxIndex+22));
+                    absoluteMinIndex = absoluteMaxIndex - 41 + relativeMinIndex;
+                    
+                    % discernes the extrema (minimum or maximum) that
+                    % occurs first
+                    firstIndex = min([absoluteMaxIndex absoluteMinIndex]);
+
                     % Q.2.1
                     spikesTimesEst(numberOfSpikes) = firstIndex;
                     % Q.2.2
-                    spikesEst(numberOfSpikes, :) = data(spikeStartIndex-1+firstIndex-34:spikeStartIndex-1+firstIndex+29);
+                    spikesEst(numberOfSpikes, :) = data(firstIndex-34:firstIndex+29);
                     break;
                 end
             end
@@ -154,13 +165,79 @@ for fileIndex=1:4
     
     fprintf('%d spikes found for dataset #%d\n', numberOfSpikes, fileIndex);
     fprintf('actual number of spikes = %d\n', length(Dataset.spikeTimes));
-    fprintf('diff = %d\n\n', abs(length(Dataset.spikeTimes) - numberOfSpikes));
+    fprintf('diff = %d\n\n', numberOfSpikes - length(Dataset.spikeTimes));
         
     figure();
     hold on;
     for spike=1:numberOfSpikes
         plot(1:64, spikesEst(spike, :));
     end
+    title(['Spikes of dataset #' num2str(fileIndex) ' aligned at the first extrema']);
+    xlabel('Samples');
+    ylabel('Trivial Unit');
     drawnow;
     hold off;
+    
+    %% Q.2.3
+    realSpikeIndex = double(Dataset.spikeTimes);
+    numberOfCorrectEstimations = 0;
+    numberOfUndetectedSpikes = 0;
+    averageEstimationError = 0;
+    
+    correctSpikes(2500, 64) = 0;
+    spikeClass(2500) = 0;
+    
+    for trueSpikeIndex=1:length(realSpikeIndex)
+        [estimationError, closestIndex] = min(abs(spikesTimesEst-realSpikeIndex(trueSpikeIndex)));
+        if estimationError < 32
+            numberOfCorrectEstimations = numberOfCorrectEstimations + 1;
+            averageEstimationError = averageEstimationError + estimationError;
+            
+            correctSpikes(numberOfCorrectEstimations, :) = spikesEst(closestIndex, :);
+            spikeClass(numberOfCorrectEstimations) = double(Dataset.spikeClass(trueSpikeIndex));
+        else
+            numberOfUndetectedSpikes = numberOfUndetectedSpikes + 1;
+        end
+    end
+    
+    averageEstimationError = averageEstimationError / numberOfSpikes;
+    
+    fprintf('Number of correct spike detections is %d\n', numberOfCorrectEstimations);
+    if numberOfSpikes-numberOfCorrectEstimations > 0
+        fprintf('Number of uncorrect spike detections is %d\n', numberOfSpikes-numberOfCorrectEstimations);
+    end
+    fprintf('Number of undetected spikes is %d\n', numberOfUndetectedSpikes);
+    fprintf('Average error of spike index estimation is %d\n\n', averageEstimationError);
+    
+    %% Q.2.4
+    features(numberOfCorrectEstimations, 5) = 0;
+    
+    for spike=1:numberOfCorrectEstimations
+        % finds index of max
+        [maxVal, features(spike, 1)] = max(correctSpikes(spike, :));
+        % calculates Vpeak-to-peak
+        features(spike, 2) = maxVal - min(correctSpikes(spike, :));
+        % calculates ZCR
+        features(spike, 3) = mean(abs(diff(sign(correctSpikes(spike, :)))));
+        % calculates the signal energy
+        features(spike, 4) = sum(correctSpikes(spike, :).^2);
+        % calculates the fft of the signal
+        asud = fft(correctSpikes(spike, :));
+        features(spike, 5) = asud(1)^2;
+    end
+    
+    figure();
+    scatter(features(:, 1), features(:, 2));
+    title(['Dataset #' num2str(fileIndex) ' feature plot']);
+    xlabel('Index of max value');
+    ylabel('Peak-to-peak amplitude');
+    
+    %% Q.2.5
+    accuracy = MyClassify(features, spikeClass');
+    fprintf('Accuracy achieved is %.2f%%\n\n', accuracy)
+    
+    clearvars = {'spikesTimesEst', 'spikesEst', 'data', 'features', 'realSpikeIndex', ...
+        'correctSpikes', 'spikeClass', 'Data'};
+    clear(clearvars{:})
+    clear clearvars
 end