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authbiomedicaltechnologyassignment2018
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Minor output changes

master
Apostolos Fanakis 6 years ago
parent
257c89d8ad
commit
8082825ef6
1 changed files with 7 additions and 6 deletions
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  1. 11
      spike_sorting.m

11
spike_sorting.m
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@ -2,8 +2,8 @@
%% EMAIL[1] : apostolof@auth.gr %% EMAIL[1] : apostolof@auth.gr
%% AUTHOR[2] : Charalampos Papadiakos (8302) %% AUTHOR[2] : Charalampos Papadiakos (8302)
%% EMAIL[2] : charaldp@ece.auth.gr %% EMAIL[2] : charaldp@ece.auth.gr
%% AUTHOR[3] : Hlektra Mitsi () %% AUTHOR[3] : Hlektra Mitsi (8536)
%% EMAIL[3] : %% EMAIL[3] : ilektraem@auth.ece.gr
%% $DATE : 28-December-2018 12:45:00 $ %% $DATE : 28-December-2018 12:45:00 $
%% $Revision : 1.00 $ %% $Revision : 1.00 $
%% DEVELOPED : 9.0.0.341360 (R2016a) %% DEVELOPED : 9.0.0.341360 (R2016a)
@ -111,6 +111,7 @@ clear(clearvars{:})
clear clearvars clear clearvars
for fileIndex=1:4 for fileIndex=1:4
fprintf('========================================================\n');
fprintf('Loading evaluation dataset no. %d\n', fileIndex); fprintf('Loading evaluation dataset no. %d\n', fileIndex);
filename = sprintf('dataset\\Data_Eval_E_%d.mat', fileIndex); filename = sprintf('dataset\\Data_Eval_E_%d.mat', fileIndex);
Dataset = load(filename); Dataset = load(filename);
@ -257,7 +258,7 @@ for fileIndex=1:4
%% Q.2.5 %% Q.2.5
accuracy = MyClassify(features, spikeClass'); accuracy = MyClassify(features, spikeClass');
fprintf('Accuracy achieved is %.2f%%\n\n', accuracy); fprintf('Accuracy achieved is %.2f%%\n', accuracy);
% clustering using DB-SCAN algorithm % clustering using DB-SCAN algorithm
% code for DB-SCAN downloaded from here: % code for DB-SCAN downloaded from here:
@ -304,7 +305,7 @@ for fileIndex=1:4
xlabel('PCA feature 1'); xlabel('PCA feature 1');
ylabel('PCA feature 2'); ylabel('PCA feature 2');
accuracy = classperf(spikeClass',dbScanClasses); accuracy = classperf(spikeClass',dbScanClasses);
fprintf('Accuracy achieved with DB-SCAN is %.2f%%\n\n', accuracy.CorrectRate*100); fprintf('Accuracy achieved with DB-SCAN is %.2f%%\n', accuracy.CorrectRate*100);
% hierarchical clustering % hierarchical clustering
distances = pdist(features(:, 6:7)); distances = pdist(features(:, 6:7));
@ -331,7 +332,7 @@ for fileIndex=1:4
xlabel('PCA feature 1'); xlabel('PCA feature 1');
ylabel('PCA feature 2'); ylabel('PCA feature 2');
accuracy = classperf(spikeClass',hierarchicalClusters); accuracy = classperf(spikeClass',hierarchicalClusters);
fprintf('Accuracy achieved with hierarchical clustering is %.2f%%\n\n', accuracy.CorrectRate*100); fprintf('Accuracy achieved with hierarchical clustering is %.2f%%\n', accuracy.CorrectRate*100);
% clustering using kmeans algorithm % clustering using kmeans algorithm
rng(1); % For reproducibility rng(1); % For reproducibility

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