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testing_script.m
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%% Binary classification Kruskal + FisherLD
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data,meta);
kwb = FeatureProcess.KruskalWallis(data,meta,3,1);
Classifier.FisherLD(kwb,1);
%% Binary classification Kruskal + MinDistEuc
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
kwb = FeatureProcess.KruskalWallis(data,meta,3,1);
Classifier.MinDistEuc(kwb,1);
%% Binary classification Kruskal + MinDistMah
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
kwb = FeatureProcess.KruskalWallis(data,meta,3,1);
Classifier.MinDistMah(kwb,1);
%% Binary classification PCA + FisherLD
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcab = FeatureProcess.PCA(data,3,1);
Classifier.FisherLD(pcab,1);
%% Binary classification PCA + MinDistEuc
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcab = FeatureProcess.PCA(data,3,1);
Classifier.MinDistEuc(pcab,1);
%% Binary classification PCA + MinDistMah
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcab = FeatureProcess.PCA(data,3,1);
Classifier.MinDistMah(pcab,1);
%% Binary classification LDA + FisherLD [Perfect classification]
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
ldab = FeatureProcess.LDA(data,3,1);
Classifier.FisherLD(ldab,1);
%% Binary classification LDA + MinDistEuc
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
ldab = FeatureProcess.LDA(data,3,1);
Classifier.MinDistEuc(ldab,1);
%% Binary classification LDA + MinDistMah
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
ldab = FeatureProcess.LDA(data,3,1);
Classifier.MinDistMah(ldab,1);
%% Binary classification LDA + SVM [Perfect classification]
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
ldab = FeatureProcess.LDA(data,3,1);
Classifier.SupportVM(ldab,1);
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%% Multiclass classification Kruskal + FisherLD
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data,meta);
kwb = FeatureProcess.KruskalWallis(data,meta,3,0);
Classifier.FisherLD(kwb,1);
%% Multiclass classification Kruskal + MinDistEuc
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
kwm = FeatureProcess.KruskalWallis(data,meta,3,0);
Classifier.MinDistEuc(kwm,1);
%% Multiclass classification Kruskal + MinDistMah
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
kwm = FeatureProcess.KruskalWallis(data,meta,3,0);
Classifier.MinDistMah(kwm,1);
%% Multiclass classification Kruskal + SVM
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
kwm = FeatureProcess.KruskalWallis(data,meta,3,0);
Classifier.SupportVM(kwm,1);
%% Multiclass classification Kruskal + Bayes
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
kwm = FeatureProcess.KruskalWallis(data,meta,3,0);
Classifier.Bayesian(kwm,1);
%% Multiclass classification Kruskal + KNN
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
kwm = FeatureProcess.KruskalWallis(data,meta,3,0);
Classifier.KNearestNeighboors(kwm,1);
%% Multiclass classification PCA + FisherLD
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcam = FeatureProcess.PCA(data,3,0);
Classifier.FisherLD(pcam,1);
%% Multiclass classification PCA + MinDistEuc
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcam = FeatureProcess.PCA(data,3,0);
Classifier.MinDistEuc(pcam,1);
%% Multiclass classification PCA + MinDistMah
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcam = FeatureProcess.PCA(data,3,0);
Classifier.MinDistMah(pcam,1);
%% Multiclass classification PCA + SVM
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcam = FeatureProcess.PCA(data,3,0);
Classifier.SupportVM(pcam,1);
%% Multiclass classification PCA + Bayes
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcam = FeatureProcess.PCA(data,3,0);
Classifier.Bayesian(pcam,1);
%% Multiclass classification PCA + KNN
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcam = FeatureProcess.PCA(data,3,0);
Classifier.KNearestNeighboors(pcam,1);
%% Multiclass classification PCA + Hybrid classifier
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
pcam = FeatureProcess.PCA(data,20,0);
Classifier.HybridClassifier(pcam,1);
%%
%%% LDA Doesn't work correctly for the multiclass scenario!
%%%
%% Multiclass classification LDA + MinDistEuc
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
ldam = FeatureProcess.LDA(data,3,0);
Classifier.MinDistEuc(ldam,1);
%% Multiclass classification LDA + SVM
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
ldam = FeatureProcess.LDA(data,3,0);
Classifier.SupportVM(ldam,1);
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%% Divide and Conquer Classifiers
%% Binary classification LDA + FisherLD [Perfect classification]
%% Multiclass classification PCA + Bayes
load read_source.mat;
[data, meta] = FeatureProcess.RemCorrelated(data, meta);
Classifier.DivideConquer(data, 75, 'Classifier.Bayesian');
%% nfeatures evolution
accuracys=[];
for i=1:120
[test_result, conf_matrix, error]=Classifier.DivideConquer(data, i, 'Classifier.Bayesian');
accuracys=[accuracys; i 1-error ];
end
plot(accuracys(:,1),accuracys(:,2))
xlabel('nFeatures')
ylabel('accuracy')