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classify_leave_one_p_out.m
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classify_leave_one_p_out.m
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% Classificação de Arritmias
%
% type : 'AAMI' ou 'AAMI2'
%
% featureSet: conjunto de características
% Pode assimir os valores:
%
% '2005Chazal', '2005Guler', 2005Song, 2006Yu, 2007Yu, 2010YeCoimbra, VCGComplexNet
%
% classifier: classificador a ser utilizado
% Pode assumir os valores:
%
% 'SVM', 'MLP', 'PNN', 'LD'
%
% Autor: Eduardo Luz
%
%
function classify_leave_one_p_out(featureSet, classifier, type, pca,outlier, indexOut)
withoutPreIndex=0;
if nargin < 3
type = 'AAMI';
pca=0;
outlier=0;
indexOut=[];
withoutPreIndex = 1;
elseif nargin < 4
pca=0;
outlier=0;
indexOut=[];
withoutPreIndex = 1;
elseif nargin < 5
outlier=0;
indexOut=[];
withoutPreIndex = 1;
elseif nargin < 6
indexOut=[];
withoutPreIndex = 1;
end
s = char(featureSet);
fileNamed = ['results\',s,'_',classifier,'_',type,'_DS1_results.tex'];
arq = fopen(fileNamed,'w');
% Tabela latex dos resultados
fprintf(arq,'\\documentclass{article}\n');
fprintf(arq,'\\usepackage{graphicx}\n');
fprintf(arq,'\\usepackage[latin1]{inputenc}\n');
fprintf(arq,'\\usepackage{tabularx}\n');
fprintf(arq,'\\usepackage{multirow}\n');
fprintf(arq,'\\newcommand{\\citep}{\\cite}\n');
fprintf(arq,'\\newcommand{\\citet}{\\cite}\n');
fprintf(arq,'\\newcommand{\\TFigure}{Fig.}\n');
fprintf(arq,'\\begin{document}\n');
fprintf(arq,'\n');
fprintf(arq,'\\begin{table*} \n');
fprintf(arq,'\\footnotesize \n');
s2 = char('\\caption{Tebela de resultados por paciente ');
s2 = [s2 s(10:end-1) '} \n'];
fprintf(arq,s2);
%fprintf(arq,' \\caption{Tebela dos registros do método} \n');
fprintf(arq,' \\label{tab:regtable} \n');
fprintf(arq,' \\begin{center} \n');
fprintf(arq,' \\begin{tabular}{|c|c|c|c|c|c|c|} \n');
fprintf(arq,' \\hline \n');
fprintf(arq,' Registro & Acc & N Se/+P/FPR & SVEB Se/+P/FPR & VEB Se/+P/FPR & F Se/+P/FPR & Q Se/+P/FPR \\\\ \n');
fprintf(arq,' \\hline \n');
% iniciliza variaveis
finalCM = zeros(5,5);
% Inicializa os registros
%registers = {'232';'101';'106';'108';'109';'112';'114';'115';'116';'118';'119';'122';'124';'201';'203';'205';'207';'208';'209';'215';'220';'223';'230';...
% '100';'103';'105';'111';'113';'117';'121';'123';'200';'202';'210';'212';'213';'214';'219';'221';'222';'228';'231';'233';'234'};
%apenas registros de DS1
registers = {'101';'106';'108';'109';'112';'114';'115';'116';'118';'119';'122';'124';'201';'203';'205';'207';'208';'209';'215';'220';'223';'230'};
% imprime a tabela de registros em arquvo .tex
%printRegisterTable(['features\' featureSet '\']);
fprintf('\n Registro | Acc | N_Se SVEB_Se VEB_Se N+P SVEB+P VEB+P \n')
for k=1:size(registers,1) % numero de registros
test_ds = [];
train_ds = [];
test_ds = str2double(registers(k));
count=1;
for j=1:size(registers,1)
if j ~= k
train_ds(count) = str2double(registers(j));
count = count +1;
end
end
%% Carrega os dados
if(strcmp(featureSet,'2004Chazal')|| strcmp(featureSet,'2004Chazal_all_filt'))
feat_folder = ['features\' featureSet '\'];
%cd(feat_folder)
% já esta sendo feita normalizacao dentro da função :
% Xnorm = X - media_treino / std_treino
if(strcmp(type,'AAMI2'))
[fs_1 fs_2 fs_3 fs_4 fs_5 fs_6 fs_7 fs_8 target] = loadDataAAMI2_chazal(feat_folder, train_ds,test_ds);
else
[fs_1 fs_2 fs_3 fs_4 fs_5 fs_6 fs_7 fs_8 target] = loadDataAAMI_chazal(feat_folder, train_ds,test_ds);
end
%primeira etapa com DS1 treino DS2 teste
% normaliza
[fs_1.train, scale_factor] = mapminmax(fs_1.train');
fs_1.test = mapminmax('apply',fs_1.test',scale_factor);
[fs_5.train, scale_factor] = mapminmax(fs_5.train');
fs_5.test = mapminmax('apply',fs_5.test',scale_factor);
fs_1.train = fs_1.train';
fs_1.test = fs_1.test';
fs_5.train = fs_5.train';
fs_5.test = fs_5.test';
fs1.train = [fs_1.train fs_5.train];
fs1.test = [fs_1.test fs_5.test];
%cd ..
%cd ..
if(pca)
[coeff index1]=applyPCA(fs1.train, 99);
fs1.train = fs1.train * coeff(:,1:index1);
fs1.test = fs1.test * coeff(:,1:index1);
end
else
feat_folder = ['features\' featureSet '\'];
%cd(feat_folder)
% já esta sendo feita normalizacao dentro da função :
% Xnorm = X - media_treino / std_treino
if(strcmp(type,'AAMI2'))
[p1d p1t p2d p2t] = loadDataAAMI2(0,feat_folder,train_ds,test_ds);
else
[p1d p1t p2d p2t] = loadDataAAMI(0,feat_folder,train_ds,test_ds);
end
if strcmp(featureSet,'2007Yu_VCG_n_2_A1_A2_A3_tq_06')
%fsel =[1 2 3 4 5 6 7 8 9 10 12 15];
%fsel =[11 12 13 14 15 16];
%fsel = [ 1 2 3 4 5 6 7 8 9 17 26 29]; % 12 features
fsel = [ 1 2 3 4 5 6 7 8 9 13 17 26 29]; % 13 features
%fsel = [1 2 3 4 5 6 7 8 9 13 17 25 26 29 ]; % 15 features
%fsel = [1 2 3 4 5 6 7 8 9 11 13 17 25 26 29 37]; % 16 melhores
%fsel = [1 2 3 4 5 6 7 8 9 11 13 16 17 25 26 29 37]; % 17 melhores features
p1d = p1d(:,fsel) ;
p2d = p2d(:,fsel) ;
end
if strcmp(featureSet,'2007Yu_modified')
fsel =[1 2 3 4 5 6 7 8 9 13 14];
p1d = p1d(:,fsel) ;
p2d = p2d(:,fsel) ;
end
if strcmp(featureSet,'2007Yu')
fsel =[1 2 3 4 5 6 7 11 12]; % selecionado com busca para frente
p1d = p1d(:,fsel) ;
p2d = p2d(:,fsel) ;
end
%primeira etapa com DS1 treino DS2 teste
fs1.train = p1d;
fs1.test = p2d;
target.train = p1t;
target.test = p2t;
[fs1.train, scale_factor] = mapminmax(fs1.train');
fs1.test = mapminmax('apply',fs1.test',scale_factor);
fs1.train = fs1.train';
fs1.test = fs1.test';
if(pca)
[coeff index1]=applyPCA(fs1.train, 99);
fs1.train = fs1.train * coeff(:,1:index1);
fs1.test = fs1.test * coeff(:,1:index1);
end
if outlier
if (outlier && ~withoutPreIndex)
fs1.train(indexOut(k).N,:)=[];
target.train(indexOut(k).N,:)=[];
%remove outliers de S (maximo 5%)
fs1.train(indexOut(k).S,:)=[];
target.train(indexOut(k).S,:)=[];
%remove outliers de V (maximo 5%)
fs1.train(indexOut(k).V,:)=[];
target.train(indexOut(k).V,:)=[];
%remove outliers de F (maximo 5%)
fs1.train(indexOut(k).F,:)=[];
target.train(indexOut(k).F,:)=[];
else
indexOut(k)=[];
%remove outliers de N (maximo 5%)
indexOut(k).N = outlierRemoval( fs1.train, target.train, 1);
fs1.train(indexOut(k).N,:)=[];
target.train(indexOut(k).N,:)=[];
%remove outliers de S (maximo 5%)
indexOut(k).S = outlierRemoval( fs1.train, target.train, 2);
fs1.train(indexOut(k).S,:)=[];
target.train(indexOut(k).S,:)=[];
%remove outliers de V (maximo 5%)
indexOut(k).V = outlierRemoval( fs1.train, target.train, 3);
fs1.train(indexOut(k).V,:)=[];
target.train(indexOut(k).V,:)=[];
%remove outliers de F (maximo 5%)
indexOut(k).F = outlierRemoval( fs1.train, target.train, 4);
fs1.train(indexOut(k).F,:)=[];
target.train(indexOut(k).F,:)=[];
%save([featureSet '_Outlier_index'], 'indexN', 'indexS', 'indexV', 'indexF');
%save([featureSet '_Outlier_lopo_index'], 'indexOut');
end
end
%cd ..
%cd ..
end
%% aplica o classificador
if(strcmp(classifier,'LD'))
cd('LD_classifier')
if(strcmp(featureSet,'2004Chazal') || strcmp(featureSet,'2004Chazal_all_filt'))
cm1 = ld_Classifier_chazal(fs_1,fs_5,target);
else
cm1 = ld_Classifier(fs1,target);
end
%fprintf('\n----------------------- Classificador LD ---------------------\n')
cd ..
elseif(strcmp(classifier,'SVM'))
cd('svm')
%[best_c,best_g,best_cv,hC] = parameter_optimization(fs1.train, target.train);
%best_c=128;
%best_c=0.1;
%best_g=1/(8*size(fs1.train,2));
best_c=0.5;
best_g=1/(8*size(fs1.train,2));
%best_c=0.5;
%best_g=0.00097656;
%[newData newTarget] = unedersampling_class1(3, fs1.train,target.train);
%[best_c,best_g,best_cv,hC] = parameter_optimization(newData,newTarget);
tic
clear cm1;
[cm1] = svm_Classifier(fs1.train,target.train,fs1.test,target.test,best_c,best_g);
toc
%fprintf('\n----------------------- Classificador SVM ---------------------\n')
cd ..
%elseif(strcmp(classifier,'PNN'))
% cd pnn
%
% [acc1 sensitivityN1 sensitivitySVEB1 sensitivityVEB1 sensitivityF1 sensitivityQ1 specificitySVEB1 specificityVEB1 specificityF1 specificityQ1] = pnn_Classifier(fs.train,target.train,fs.test,target.test);
% [acc2 sensitivityN2 sensitivitySVEB2 sensitivityVEB2 sensitivityF2
% sensitivityQ2 specificitySVEB2 specificityVEB2 specificityF2 specificityQ2] = svm_Classifier(fs.train,target.train,fs.test,target.test);%
% cd ..
elseif(strcmp(classifier,'MLP'))
cd('mlp')
[cm1] = mlp_Classifier(fs1.train,target.train,fs1.test,target.test);
%fprintf('\n----------------------- Classificador MLP comb ---------------------\n')
cd ..
end
% Calcula estatíSticas
cm1
finalCM = finalCM + cm1;
acc_num=0;
acc_den=0;
den1=0;
den2=0;
num=0;
t=0;
if(size(cm1,1)>=1)
t = 1;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityN = (num/den1) * 100;
else
sensitivityN = -1;
end
if(den2~=0)
specificityN = (num/den2) * 100;
else
specificityN = -1;
end
if(TN + FP > 0)
FPR_N = 100*FP/(TN+FP);
else
FPR_N=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityN = -1;
specificityN = -1;
FPR_N=-1;
end
% caso especial para LD classifier
%size(cm1,1)==5
if(size(cm1,1)>=2)
t = 2;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivitySVEB = (num/den1) * 100;
else
sensitivitySVEB = -1;
end
if(den2~=0)
specificitySVEB = (num/den2) * 100;
else
specificitySVEB = -1;
end
if(TN + FP > 0)
FPR_SVEB = 100*FP/(TN+FP);
else
FPR_SVEB=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivitySVEB = -1;
specificitySVEB = -1;
FPR_SVEB=-1;
end
if(size(cm1,1)>=3)
t = 3;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityVEB = (num/den1) * 100;
else
sensitivityVEB = -1;
end
if(den2~=0)
specificityVEB = (num/den2) * 100;
else
specificityVEB = -1;
end
if(TN + FP > 0)
FPR_VEB = 100*FP/(TN+FP);
else
FPR_VEB=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityVEB = -1;
specificityVEB = -1;
FPR_VEB=-1;
end
if(size(cm1,1)>=4)
t = 4;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityF = (num/den1) * 100;
else
sensitivityF = -1;
end
if(den2~=0)
specificityF = (num/den2) * 100;
else
specificityF = -1;
end
if(TN + FP > 0)
FPR_F = 100*FP/(TN+FP);
else
FPR_F=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityF = -1;
specificityF = -1;
FPR_F=-1;
end
if(size(cm1,1)>=5)
t = 5;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityQ = (num/den1) * 100;
else
sensitivityQ = -1;
end
if(den2~=0)
specificityQ = (num/den2) * 100;
else
specificityQ = -1;
end
if(TN + FP > 0)
FPR_Q = 100*FP/(TN+FP);
else
FPR_Q=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityQ = -1;
specificityQ = -1;
FPR_Q=-1;
end
fprintf(arq,'%6d & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f \\\\ \n',...
str2double(registers(k)), 100*acc_num/acc_den, sensitivityN,specificityN,FPR_N,sensitivitySVEB,specificitySVEB,FPR_SVEB,sensitivityVEB,specificityVEB,FPR_VEB,...
sensitivityF,specificityF,FPR_F, sensitivityQ,specificityQ,FPR_Q);
fprintf(arq,'\n');
fprintf('\n');
fprintf('Registro=%6.0f | Acc=%6.1f |', str2double(registers(k)), 100*acc_num/acc_den);
fprintf(' N_Se=%6.1f SVEB_Se=%6.1f VEB_Se=%6.1f N+P=%6.1f SVEB+P=%6.1f VEB+P=%6.1f \n\n',...
sensitivityN, sensitivitySVEB,sensitivityVEB, specificityN, specificitySVEB,specificityVEB)
end % for t
fprintf(arq,' \\hline \n');
%fprintf(arq,' \\hline \n');
TN = sum(sum(finalCM(:,:))) - sum(finalCM(1,:)) - sum(finalCM(:,1)) + finalCM(1,1);
FP = sum(finalCM(:,1)) - finalCM(1,1);
FPR_N = FP/(TN+FP);
TN = sum(sum(finalCM(:,:))) - sum(finalCM(2,:)) - sum(finalCM(:,2)) + finalCM(2,2);
FP = sum(finalCM(:,2)) - finalCM(2,2);
FPR_SVEB = FP/(TN+FP);
TN = sum(sum(finalCM(:,:))) - sum(finalCM(3,:)) - sum(finalCM(:,3)) + finalCM(3,3);
FP = sum(finalCM(:,3)) - finalCM(3,3);
FPR_VEB = FP/(TN+FP);
if size(finalCM,1) > 3
TN = sum(sum(finalCM(:,:))) - sum(finalCM(4,:)) - sum(finalCM(:,4)) + finalCM(4,4);
FP = sum(finalCM(:,4)) - finalCM(4,4);
FPR_F = FP/(TN+FP);
TN = sum(sum(finalCM(:,:))) - sum(finalCM(5,:)) - sum(finalCM(:,5)) + finalCM(5,5);
FP = sum(finalCM(:,5)) - finalCM(5,5);
FPR_Q = FP/(TN+FP);
fprintf(arq, ' Gross & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f \\\\ \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3)+finalCM(4,4)+finalCM(5,5))/(sum(finalCM(1,:))+sum(finalCM(2,:))+sum(finalCM(3,:))+sum(finalCM(4,:))+sum(finalCM(5,:))) ,...
100*finalCM(1,1)/sum(finalCM(1,:)), 100*finalCM(1,1)/sum(finalCM(:,1)),100*FPR_N,...
100*finalCM(2,2)/sum(finalCM(2,:)),100*finalCM(2,2)/sum(finalCM(1:end-1,2)),100*FPR_SVEB,...
100*finalCM(3,3)/sum(finalCM(3,:)),100*finalCM(3,3)/sum(finalCM(1:end-2,3)),100*FPR_VEB,...
100*finalCM(4,4)/sum(finalCM(4,:)),100*finalCM(4,4)/sum(finalCM(:,4)),100*FPR_F,...
100*finalCM(4,4)/sum(finalCM(5,:)),100*finalCM(5,5)/sum(finalCM(:,5)),100*FPR_Q);
else
fprintf(arq, ' Gross & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & - & - \\\\ \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3))/(sum(finalCM(1,:))+sum(finalCM(2,:))+sum(finalCM(3,:))) ,...
100*finalCM(1,1)/sum(finalCM(1,:)), 100*finalCM(1,1)/sum(finalCM(:,1)),100*FPR_N,...
100*finalCM(2,2)/sum(finalCM(2,:)),100*finalCM(2,2)/sum(finalCM(:,2)),100*FPR_SVEB,...
100*finalCM(3,3)/sum(finalCM(3,:)),100*finalCM(3,3)/sum(finalCM(:,3)),100*FPR_VEB);
end
fprintf(arq,' \\hline \n');
fprintf(arq,' \\end{tabular} \n');
fprintf(arq,' \\end{center} \n');
fprintf(arq,'\\end{table*} \n');
fprintf(arq,'\n');
fprintf(arq,'\n');
fprintf(arq,'\n');
fprintf(arq,'\\begin{table*} \n');
fprintf(arq,' \\caption{Matriz e confusão} \n');
fprintf(arq,' \\label{tab:regtable} \n');
fprintf(arq,' \\begin{center} \n');
fprintf(arq,' \\begin{tabular}{|');
for tt=1:size(finalCM,1)
fprintf(arq,'c|');
end
fprintf(arq,'} \n');
fprintf(arq,'\n');
fprintf(arq,' \\hline \n');
for tt=1:size(finalCM,1)
for uu=1:size(finalCM,2)
if uu==5
fprintf(arq,'%6.0f',finalCM(tt,uu));
else
fprintf(arq,'%6.0f & ',finalCM(tt,uu));
end
end
fprintf(arq,' \\\\ \n');
fprintf(arq,' \\hline \n');
end
fprintf(arq,' \\hline \n');
fprintf(arq,' \\end{tabular} \n');
fprintf(arq,' \\end{center} \n');
fprintf(arq,'\\end{table*} \n');
fprintf(arq,'\n');
fprintf(arq,'\n');
fprintf(arq,'\\end{document}\n');
fclose(arq);
fprintf('\n Gross Statistics:\n');
fprintf(' Gross & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f \\\\ \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3)+finalCM(4,4)+finalCM(5,5))/(sum(finalCM(1,:))+sum(finalCM(2,:))+sum(finalCM(3,:))+sum(finalCM(4,:))+sum(finalCM(5,:))) ,...
100*finalCM(1,1)/sum(finalCM(1,:)), 100*finalCM(2,2)/sum(finalCM(2,:)), 100*finalCM(3,3)/sum(finalCM(3,:)), 100*finalCM(4,4)/sum(finalCM(4,:)), 100*finalCM(5,5)/sum(finalCM(5,:)),...
100*finalCM(1,1)/sum(finalCM(:,1)), 100*finalCM(2,2)/sum(finalCM(:,2)), 100*finalCM(3,3)/sum(finalCM(:,3)), 100*finalCM(4,4)/sum(finalCM(:,4)), 100*finalCM(5,5)/sum(finalCM(:,5)));
if outlier
save([featureSet '_Outlier_lopo_index'], 'indexOut');
end
end