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classify_hierarq.m
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classify_hierarq.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
%
% testar best c=128 g=0.0625 rate=55.3843%)
%
function classify_hierarq(featureSet, classifier, type, param1, param2, param3)
if nargin < 3
type = 'AAMI';
param1 = 0;
param2 = 0;
param3 = 0;
elseif nargin < 4
%C = 0;
%gamma = 0; % default
% default
param1 = 0;
param2 = 0;
param3 = 0;
elseif nargin < 5
param2 = 0;
param3 = 0;
elseif nargin < 6
param3 = 0;
end
s = char(featureSet);
fileNamed = ['results\',s,'\',classifier,'_test_ds2_HIERARQUICO.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 '} \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|c|c|} \n');
fprintf(arq,' \\hline \n');
fprintf(arq,' Registro & Acc & N Se/+P/FPR & ARRH Se/+P/FPR & ACC etapa 2 & 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(2,2);
finalCM_2= zeros(4,4);
% Inicializa os registros
DS1 = {'101';'106';'108';'109';'112';'114';'115';'116';'118';'119';'122';'124';'201';'203';'205';'207';'208';'209';'215';'220';'223';'230'};
DS2 = {'100';'103';'105';'111';'113';'117';'121';'123';'200';'202';'210';'212';'213';'214';'219';'221';'222';'228';'231';'232';'233';'234'};
%DS1 = {'101';'106'};
%DS2 = {'100';'103';'105';'111';'113';'117'};
% imprime a tabela de registros em arquvo .tex
%printRegisterTable(['features\' featureSet '\']);
%printRegisterTable_15_classes(['features\' featureSet '\']);
test_ds = [];
train_ds = [];
for j=1:size(DS1,1)
train_ds(j) = str2double(DS1(j));
end
first_time=1;
for k=1:size(DS2,1) % numero de registros
test_ds = str2double(DS2(k));
%% Carrega os dados
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_step_1 p1t_step_1 p2d_step_1 p2t_step_1] = loadData_CH_step_1(0,feat_folder,train_ds,test_ds);
[p1d_step_2 p1t_step_2 p2d_step_2 p2t_step_2] = loadData_CH_step_2(0,feat_folder,train_ds,test_ds);
end
if strcmp(featureSet,'VCG_n_2_64_64_A1_A2_A4_H_P_RR_PT_Penerg_QRSwid_autocorr')
fsel =[ 1 2 6 8 16 17 19 20]; % 8 melhores features
p1d_step_1 = p1d_step_1(:,fsel) ;
p2d_step_1 = p2d_step_1(:,fsel) ;
p1d_step_2 = p1d_step_2(:,fsel) ;
p2d_step_2 = p2d_step_2(:,fsel) ;
end
if strcmp(featureSet,'2007Yu_VCG_n_2_A1_A2_A3_tq_06')
%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_step_1 = p1d_step_1(:,fsel) ;
p2d_step_1 = p2d_step_1(:,fsel) ;
p1d_step_2 = p1d_step_2(:,fsel) ;
p2d_step_2 = p2d_step_2(:,fsel) ;
end
% if strcmp(featureSet,'2007Yu')
% fsel =[1 2 3 4 5 6 7 11 12];
% p1d_step_1 = p1d_step_1(:,fsel) ;
% p2d_step_1 = p2d_step_1(:,fsel) ;
% p1d_step_2 = p1d_step_2(:,fsel) ;
% p2d_step_2 = p2d_step_2(:,fsel) ;
% end
if strcmp(featureSet,'VCG_n_2_32_32_A2_A4_RR_PT_Penerg_QRSwid')
%fsel =[1 2 3 4 5 6 7 11 12];
fsel =[1 2 3 4 5 9 17 20 23];
p1d_step_1 = p1d_step_1(:,fsel) ;
p2d_step_1 = p2d_step_1(:,fsel) ;
p1d_step_2 = p1d_step_2(:,fsel) ;
p2d_step_2 = p2d_step_2(:,fsel) ;
end
%primeira etapa com DS1 treino DS2 teste
fs1.train = p1d_step_1;
fs1.test = p2d_step_1;
target.train = p1t_step_1;
target.test = p2t_step_1;
[fs1.train, scale_factor] = mapminmax(fs1.train');
fs1.test = mapminmax('apply',fs1.test',scale_factor);
fs1.train = fs1.train';
fs1.test = fs1.test';
%primeira etapa com DS1 treino DS2 teste
fs2.train = p1d_step_2;
fs2.test = p2d_step_2;
target2.train = p1t_step_2;
target2.test = p2t_step_2;
[fs2.train, scale_factor] = mapminmax(fs2.train');
fs2.test = mapminmax('apply',fs2.test',scale_factor);
fs2.train = fs2.train';
fs2.test = fs2.test';
%cd ..
%cd ..
%% aplica o classificador
if(strcmp(classifier,'SVM'))
cd('svm')
tic
if(first_time==1)
first_time=0;
if(param1==0)
%[best_c,best_g,best_cv,hC] = parameter_optimization(fs1.train, target.train);
best_c=0.5;
best_g=1/(8*size(fs1.train,2));
best_c_2=0.5;
best_g_2=best_g;
elseif(param1>0)
best_c=param1;
best_g=param2;
best_c_2=param1;
best_g_2=param2;
elseif(param1==-1)
% ***** CLASSIFICA PRIMEIRA ETAPA
[newData newTarget] = undersampling_all_class(20, fs1.train,target.train);
[best_c,best_g,best_cv,hC] = parameter_optimization_CH_1(newData,newTarget);
[newData2 newTarget2] = undersampling_all_class(20,fs2.train,target2.train);
[best_c_2,best_g_2,best_cv,hC] = parameter_optimization_CH_2(newData2,newTarget2);
end
%[newData newTarget] = SMOTE(fs1.train, target.train, 2, 4000, 5);
[cm1 model_step_1 predict_matrix label_matrix] = svm_Classifier_CH_1(fs1.train,target.train,fs1.test,target.test,best_c,best_g);
%[cm1 model_step_1 predict_matrix label_matrix] = svm_Classifier_CH_1(newData, newTarget,fs1.test,target.test,best_c,best_g);
% ***** CLASSIFICA SEGUNDA ETAPA
%[newData newTarget] = undersampling_all_class(10,
%fs2.train,target2.train);
%[best_c_2,best_g_2,best_cv,hC] = parameter_optimization_CH_2(fs2.train,target2.train);
%[newData newTarget] = SMOTE(fs1.train, target.train, 2, 4000, 5);
gg=find(label_matrix(:,2)==1);
hh=find(predict_matrix(:,2)==1);
anormal_id = find(predict_matrix(:,2)==1 & label_matrix(:,2));
%anormal_id = [anormal_id;zeros(1,size(gg,1)-size(anormal_id,1))'];%completa com zero
indice=[];
for i=1:size(anormal_id,1)
indice(i) = find(gg==anormal_id(i));
end
[cm2 model_step_2] = svm_Classifier_CH_2(fs2.train,target2.train,fs2.test(indice,:),target2.test(indice,:),best_c_2,best_g_2);
else
% ***** CLASSIFICA PRIMEIRA ETAPA
testingLabels = [];
targetsTest = [];
% formata para uso em SVM - para cada classe
for t=1:size(target.test,2)
targetsTest(find(target.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
[predict_label, accuracy, dec_values] = svmpredict(testingLabels', fs1.test, model_step_1); % test the training data
%cm1 = confusionmat(testingLabels',predict_label);
clear predict_matrix;
for i=1: size(predict_label,1)
switch(predict_label(i))
case 1,
predict_matrix(i,:) = [1 0];
case 2,
predict_matrix(i,:) = [0 1];
end
end
clear label_matrix;
for i=1: size(testingLabels,2)
switch(testingLabels(i))
case 1,
label_matrix(i,:) = [1 0];
case 2,
label_matrix(i,:) = [0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
[c cm1] = confusion(label_matrix', predict_matrix');
% **** CLASSIFICA SEGUNDA ETAPA
% so classificaco quem foi considerado arritmico na etapa
% anterior
gg=find(label_matrix(:,2)==1);
hh=find(predict_matrix(:,2)==1);
anormal_id = find(predict_matrix(:,2)==1 & label_matrix(:,2));
%anormal_id = [anormal_id;zeros(1,size(gg,1)-size(anormal_id,1))'];
testingLabels = [];
targetsTest = [];
% formata para uso em SVM - para cada classe
for t=1:size(target2.test,2)
targetsTest(find(target2.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
indice=[];
for i=1:size(anormal_id,1)
indice(i) = find(gg==anormal_id(i));
end
[predict_label_2, accuracy_2, dec_values_2] = svmpredict(testingLabels(indice)', fs2.test(indice,:), model_step_2); % test the training data
%cm1 = confusionmat(testingLabels',predict_label);
clear predict_matrix_2;
predict_matrix_2=[];
for i=1: size(predict_label_2,1)
switch(predict_label_2(i))
case 1,
predict_matrix_2(i,:) = [1 0 0 0];
case 2,
predict_matrix_2(i,:) = [0 1 0 0];
case 3,
predict_matrix_2(i,:) = [0 0 1 0];
case 4,
predict_matrix_2(i,:) = [0 0 0 1];
end
end
tLabes = testingLabels(indice);
clear label_matrix_2;
label_matrix_2=[];
for i=1: size(tLabes,2)
switch(tLabes(i))
case 1,
label_matrix_2(i,:) = [1 0 0 0];
case 2,
label_matrix_2(i,:) = [0 1 0 0];
case 3,
label_matrix_2(i,:) = [0 0 1 0];
case 4,
label_matrix_2(i,:) = [0 0 0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
[c2 cm2] = confusion(label_matrix_2', predict_matrix_2');
end
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')
if(first_time==1)
first_time=0;
% ***** CLASSIFICA PRIMEIRA ETAPA
[cm1 net1_step1 net2_step1 net3_step1 net4_step1 combNet_step1 predict_matrix label_matrix] = mlp_Classifier(fs1.train,target.train,fs1.test,target.test);
% ***** CLASSIFICA SEGUNDA ETAPA (apenas os batimentos classificados como arritmicos vão para o segundo classificador)
gg=find(label_matrix(:,2)==1);
hh=find(predict_matrix(:,2)==1);
anormal_id = find(predict_matrix(:,2)==1 & label_matrix(:,2));
indice=[];
for i=1:size(anormal_id,1)
indice(i) = find(gg==anormal_id(i));
end
[cm2 net1_step2 net2_step2 net3_step2 net4_step2 combNet_step2] = mlp_Classifier(fs2.train,target2.train,fs2.test(indice,:),target2.test(indice,:));
else
% ***** CLASSIFICA PRIMEIRA ETAPA
testingLabels = [];
targetsTest = [];
% formata para uso em SVM - para cada classe
for t=1:size(target.test,2)
targetsTest(find(target.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
%[predict_label, accuracy, dec_values] = svmpredict(testingLabels', fs1.test, model_step_1); % test the training data
%cm1 = confusionmat(testingLabels',predict_label);
outputs1 = sim(net1_step1,fs1.test');
outputs2 = sim(net2_step1,fs1.test');
outputs3 = sim(net3_step1,fs1.test');
outputs4 = sim(net4_step1,fs1.test');
newInputTest = [outputs1;outputs2;outputs3;outputs4];
predict_label = sim(combNet_step1,newInputTest);
predict_label = predict_label';
clear predict_matrix;
predict_matrix = round(predict_label);
%predict_matrix=[];
%for i=1: size(predict_label,1)
%switch(predict_label(i))
% case 1,
% predict_matrix(i,:) = [1 0];
% case 2,
% predict_matrix(i,:) = [0 1];
%end
%end
clear label_matrix;
label_matrix=[];
for i=1: size(testingLabels,2)
switch(testingLabels(i))
case 1,
label_matrix(i,:) = [1 0];
case 2,
label_matrix(i,:) = [0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
[c cm1] = confusion(label_matrix', predict_matrix');
% **** CLASSIFICA SEGUNDA ETAPA
% so classificaco quem foi considerado arritmico na etapa
% anterior
gg=find(label_matrix(:,2)==1);
hh=find(predict_matrix(:,2)==1);
anormal_id = find(predict_matrix(:,2)==1 & label_matrix(:,2));
%anormal_id = [anormal_id;zeros(1,size(gg,1)-size(anormal_id,1))'];
testingLabels = [];
targetsTest = [];
% formata para uso em SVM - para cada classe
for t=1:size(target2.test,2)
targetsTest(find(target2.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
indice=[];
for i=1:size(anormal_id,1)
indice(i) = find(gg==anormal_id(i));
end
%[predict_label_2, accuracy_2, dec_values_2] = svmpredict(testingLabels(indice)', fs2.test(indice,:), model_step_2); % test the training data
%cm1 = confusionmat(testingLabels',predict_label);
outputs1 = sim(net1_step2,fs2.test(indice,:)');
outputs2 = sim(net2_step2,fs2.test(indice,:)');
outputs3 = sim(net3_step2,fs2.test(indice,:)');
outputs4 = sim(net4_step2,fs2.test(indice,:)');
newInputTest = [outputs1;outputs2;outputs3;outputs4];
predict_label_2 = sim(combNet_step2,newInputTest);
predict_label_2 = predict_label_2';
predict_label_2 = round(predict_label_2);
clear predict_matrix_2;
predict_matrix_2 = round(predict_label_2);
%predict_matrix_2=[];
% for i=1: size(predict_label_2,1)
% switch(predict_label_2(i))
% case 1,
% predict_matrix_2(i,:) = [1 0 0 0];
% case 2,
% predict_matrix_2(i,:) = [0 1 0 0];
% case 3,
% predict_matrix_2(i,:) = [0 0 1 0];
% case 4,
% predict_matrix_2(i,:) = [0 0 0 1]; %
%end
%end
tLabes = testingLabels(indice);
clear label_matrix_2;
label_matrix_2=[];
for i=1: size(tLabes,2)
switch(tLabes(i))
case 1,
label_matrix_2(i,:) = [1 0 0 0];
case 2,
label_matrix_2(i,:) = [0 1 0 0];
case 3,
label_matrix_2(i,:) = [0 0 1 0];
case 4,
label_matrix_2(i,:) = [0 0 0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
if size(label_matrix_2,2)==0 || size(predict_matrix_2,2)==0
cm2 = zeros(4,4);
c2=0;
else
[c2 cm2] = confusion(label_matrix_2', predict_matrix_2');
end
end
%fprintf('\n----------------------- Classificador MLP comb ---------------------\n')
cd ..
elseif(strcmp(classifier,'PNN'))
cd('pnn')
if(first_time==1)
first_time=0;
if param1 == 0
spread = 0.1;
else
spread = param1;
end
[cm1 net1 predict_matrix label_matrix] = classifyPNN_ch1(fs1.train,target.train,fs1.test,target.test, spread);
% ***** CLASSIFICA SEGUNDA ETAPA (apenas os batimentos classificados como arritmicos vão para o segundo classificador)
gg=find(label_matrix(:,2)==1);
hh=find(predict_matrix(:,2)==1);
predict_matrix=predict_matrix';
anormal_id = find(predict_matrix(:,2)==1 & label_matrix(:,2));
indice=[];
for i=1:size(anormal_id,1)
indice(i) = find(gg==anormal_id(i));
end
[cm2 net2] = classifyPNN_ch2(fs2.train,target2.train,fs2.test(indice,:),target2.test(indice,:), spread);
else
% ***** CLASSIFICA PRIMEIRA ETAPA
testingLabels = [];
targetsTest = [];
% formata para uso em SVM - para cada classe
for t=1:size(target.test,2)
targetsTest(find(target.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
for(i=1:length(fs1.test(:,1)))
for(j=1:length(fs1.test(1,:)))
%normaliza tb a matriz de teste
fs1.test(i,j) = tansig((fs1.test(i,j)-mean(fs1.test(i,:)))/std(fs1.test(i,:)));
end
end
%predict_label = sim(combNet_step1,newInputTest);
predict_label = sim(net1,fs1.test');
predict_label = predict_label';
clear predict_matrix;
predict_matrix=[];
for t=1:2
predict_matrix(find(predict_label(:,t)==1),t)=1;
end
%predict_label_vec = sum(predict_label_vec);
%clear predict_matrix;
%predict_matrix=[];
%for i=1: size(predict_label,1)
%switch(predict_label(i))
% case 1,
% predict_matrix(i,:) = [1 0];
% case 2,
% predict_matrix(i,:) = [0 1];
%end
%end
clear label_matrix;
label_matrix=[];
for i=1: size(testingLabels,2)
switch(testingLabels(i))
case 1,
label_matrix(i,:) = [1 0];
case 2,
label_matrix(i,:) = [0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
[c cm1] = confusion(label_matrix', predict_matrix');
% **** CLASSIFICA SEGUNDA ETAPA
% so classificaco quem foi considerado arritmico na etapa
% anterior
gg=find(label_matrix(:,2)==1);
hh=find(predict_matrix(:,2)==1);
%anormal_id = find(predict_matrix(:,2)==1 & label_matrix(:,2));
anormal_id = find(predict_matrix(:,2)==1 & label_matrix(:,2));
testingLabels = [];
targetsTest = [];
% formata para uso em SVM - para cada classe
for t=1:size(target2.test,2)
targetsTest(find(target2.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
indice=[];
for i=1:size(anormal_id,1)
indice(i) = find(gg==anormal_id(i));
end
for(i=1:length(fs2.test(:,1)))
for(j=1:length(fs2.test(1,:)))
%normaliza tb a matriz de teste
fs2.test(i,j) = tansig((fs2.test(i,j)-mean(fs2.test(i,:)))/std(fs2.test(i,:)));
end
end
%predict_label = sim(combNet_step1,newInputTest);
predict_label_2 = sim(net2,fs2.test(indice,:)');
predict_label_2 = predict_label_2';
clear predict_matrix_2;
predict_matrix_2=[];
for t=1:4
predict_matrix_2(find(predict_label_2(:,t)==1),t)=1;
end
% predict_label_2_vec = sum(predict_label_2_vec);
%clear predict_matrix_2;
%predict_matrix_2=[];
%predict_label_2 = predict_label_2';
% for i=1: size(predict_label_2,1)
% switch(predict_label_2(i))
% case 1,
% predict_matrix_2(i,:) = [1 0 0 0];
% case 2,
% predict_matrix_2(i,:) = [0 1 0 0];
% case 3,
% predict_matrix_2(i,:) = [0 0 1 0];
% case 4,
% predict_matrix_2(i,:) = [0 0 0 1];
% end
% end
tLabes = testingLabels(indice);
clear label_matrix_2;
label_matrix_2=[];
for i=1: size(tLabes,2)
switch(tLabes(i))
case 1,
label_matrix_2(i,:) = [1 0 0 0];
case 2,
label_matrix_2(i,:) = [0 1 0 0];
case 3,
label_matrix_2(i,:) = [0 0 1 0];
case 4,
label_matrix_2(i,:) = [0 0 0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
%[c2 cm2] = confusion(label_matrix_2', predict_matrix_2');
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
if size(label_matrix_2,2)==0 || size(predict_matrix_2,2)==0
cm2 = zeros(4,4);
c2=0;
else
[c2 cm2] = confusion(label_matrix_2', predict_matrix_2');
end
end
cd ..
end
% ***** Calcula estatíSticas PRIMEIR ETAPA
cm1
finalCM = finalCM + cm1;
acc_num=0;
acc_den=0;
den1=0;
den2=0;
num=0;
t=0;
TN=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)
sensitivityARRH = (num/den1) * 100;
else
sensitivityARRH = -1;
end
if(den2~=0)
specificityARRH = (num/den2) * 100;
else
specificityARRH = -1;
end
if(TN + FP > 0)
FPR_ARRH = 100*FP/(TN+FP);
else
FPR_ARRH=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityARRH = -1;
specificityARRH = -1;
FPR_ARRH=-1;
end
fprintf(arq,'%6d & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f &',...
str2double(DS2(k)), 100*acc_num/acc_den, sensitivityN, specificityN,FPR_N, sensitivityARRH,specificityARRH,FPR_ARRH);
%fprintf(arq,'\n');
fprintf('\n');
fprintf('Registro=%6.0f | Acc=%6.1f |', str2double(DS2(k)), 100*acc_num/acc_den);
fprintf(' N_Se=%6.1f N+P=%6.1f ARRH_Se=%6.1f ARRH+P=%6.1f ',...
sensitivityN, specificityN, sensitivityARRH, specificityARRH)
cm1=[];
% **** Calcula estatíSticas PARA SEGUNDA ETAPA
cm2
if isempty(cm2)
cm2 = zeros(4,4);
end
finalCM_2 = finalCM_2 + cm2;
acc_num=0;
acc_den=0;
den1=0;
den2=0;
num=0;
t=0;
% caso especial para LD classifier
%size(cm1,1)==5
if(size(cm2,1)>=1)
t = 1;
num = cm2(t,t);
den1 = sum(cm2(t,:));
den2 = sum(cm2(:,t));
TN = sum(sum(cm2(:,:))) - den1 - den2 + cm2(t,t);
FP = den2 - cm2(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(cm2,1)>=2)
t = 2;
num = cm2(t,t);
den1 = sum(cm2(t,:));
den2 = sum(cm2(:,t));
TN = sum(sum(cm2(:,:))) - den1 - den2 + cm2(t,t);
FP = den2 - cm2(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(cm2,1)>=3)
t = 3;
num = cm2(t,t);
den1 = sum(cm2(t,:));
den2 = sum(cm2(:,t));
TN = sum(sum(cm2(:,:))) - den1 - den2 + cm2(t,t);
FP = den2 - cm2(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(cm2,1)>=4)
t = 4;
num = cm2(t,t);
den1 = sum(cm2(t,:));
den2 = sum(cm2(:,t));
TN = sum(sum(cm2(:,:))) - den1 - den2 + cm2(t,t);
FP = den2 - cm2(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,' %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*acc_num/acc_den, sensitivitySVEB,specificitySVEB,FPR_SVEB,sensitivityVEB,specificityVEB,FPR_VEB,...
sensitivityF,specificityF,FPR_F, sensitivityQ,specificityQ,FPR_Q);
fprintf(arq,'\n');
%fprintf('\n');
fprintf('Acc etapa 2 =%6.1f |', 100*acc_num/acc_den);
fprintf(' SVEB_Se=%6.1f SVEB+P=%6.1f VEB_Se=%6.1f VEB+P=%6.1f \n\n',...
sensitivitySVEB,specificitySVEB,sensitivityVEB,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_ARRH = FP/(TN+FP);
fprintf(arq, ' Gross & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f ',...
100*(finalCM(1,1)+finalCM(2,2))/(sum(finalCM(1,:))+sum(finalCM(2,:))) ,...
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_ARRH);
TN = sum(sum(finalCM_2(:,:))) - sum(finalCM_2(1,:)) - sum(finalCM_2(:,1)) + finalCM_2(1,1);
FP = sum(finalCM_2(:,1)) - finalCM_2(1,1);
FPR_SVEB = FP/(TN+FP);
TN = sum(sum(finalCM_2(:,:))) - sum(finalCM_2(2,:)) - sum(finalCM_2(:,2)) + finalCM_2(2,2);
FP = sum(finalCM_2(:,2)) - finalCM_2(2,2);
FPR_VEB = FP/(TN+FP);
TN = sum(sum(finalCM_2(:,:))) - sum(finalCM_2(3,:)) - sum(finalCM_2(:,3)) + finalCM_2(3,3);
FP = sum(finalCM_2(:,3)) - finalCM_2(3,3);
FPR_F = FP/(TN+FP);
TN = sum(sum(finalCM_2(:,:))) - sum(finalCM_2(4,:)) - sum(finalCM_2(:,4)) + finalCM_2(4,4);
FP = sum(finalCM_2(:,4)) - finalCM_2(4,4);
FPR_Q = FP/(TN+FP);
fprintf(arq, '& %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_2(1,1)+finalCM_2(2,2)+finalCM_2(3,3)+finalCM_2(4,4))/(sum(finalCM_2(1,:))+sum(finalCM_2(2,:))+sum(finalCM_2(3,:))+sum(finalCM_2(4,:))) ,...
100*finalCM_2(1,1)/sum(finalCM_2(1,:)),100*finalCM_2(1,1)/sum(finalCM_2(1:end-1,1)),100*FPR_SVEB,...
100*finalCM_2(2,2)/sum(finalCM_2(2,:)),100*finalCM_2(2,2)/sum(finalCM_2(1:end-2,2)),100*FPR_VEB,...
100*finalCM_2(3,3)/sum(finalCM_2(3,:)),100*finalCM_2(3,3)/sum(finalCM_2(:,3)),100*FPR_F,...
100*finalCM_2(4,4)/sum(finalCM_2(4,:)),100*finalCM_2(4,4)/sum(finalCM_2(:,4)),100*FPR_Q);
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 etapa 1} \n');
fprintf(arq,' \\label{tab:regtable1} \n');
fprintf(arq,' \\begin{center} \n');