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classify_leave_one_p_out.asv
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classify_leave_one_p_out.asv
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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_leave_one_p_out(featureSet, classifier, type)
if nargin < 3
type = 'AAMI';
end
s = char(featureSet);
fileNamed = ['results\',s,'_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');
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|c|c|c|c|c|} \n');
fprintf(arq,' \\hline \n');
fprintf(arq,' Registro & Acc & N Se & SVEB Se & VEB Se & F Se & Q Se & N+P & SVEB+P & VEB+P & F+P & Q+P \\\\ \n');
fprintf(arq,' \\hline \n');
% iniciliza variaveis
finalCM = zeros(5,5);
% Inicializa os registros
%registers = {'105';'100';'103';'111';'113';'117';'121';'123';'200';'202';'210';'212';'213';'214';'219';'221';'222';'228';'231';'232';'233';'234';'101';'106';'108';'109';'112';'114';'115';'116';'118';'119';'122';'124';'201';'203';'205';'207';'208';'209';'215';'220';'223';'230'};
registers = {'105';'100';'103';'111';'113';'117';};
%registers = {'232';'209';'115';'105';'100';'103';'111';'113';'117';'121';'123';'200';'202';'210';'212';'213';'214';'219';'221';'222';'228';'231';'233';'234';'101';'106';'108';'109';'112';'114';'116';'118';'119';'122';'124';'201';'203';'205';'207';'208';'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_da = [];
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'))
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
fs1.train = [fs_3.train fs_7.train];
fs1.test = [fs_3.test fs_7.test];
%cd ..
%cd ..
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
%primeira etapa com DS1 treino DS2 teste
fs1.train = p1d;
fs1.test = p2d;
target.train = p1t;
target.test = p2t;
%cd ..
%cd ..
end
%% aplica o classificador
if(strcmp(classifier,'LD'))
cd('LD_classifier')
if(strcmp(featureSet,'2004Chazal'))
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=1;
best_g=0.0625;
%[newData newTarget] = unedersampling_class1(5, fs1.train,target.train);
%[best_c,best_g,best_cv,hC] = parameter_optimization(newData,newTarget);
tic
[cm1] = svm_Classifier(fs1.train,target.train,fs1.test,target.test);
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));
if(den1~=0)
sensitivityN = (num/den1) * 100;
else
sensitivityN = 0;
end
if(den2~=0)
specificityN = (num/den2) * 100;
else
specificityN = 0;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityN = -1;
specificityN = -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));
if(den1~=0)
sensitivitySVEB = (num/den1) * 100;
else
sensitivitySVEB = 0;
end
if(den2~=0)
specificitySVEB = (num/den2) * 100;
else
specificitySVEB = 0;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivitySVEB = -1;
specificitySVEB = -1;
end
if(size(cm1,1)>=3)
t = 3;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
if(den1~=0)
sensitivityVEB = (num/den1) * 100;
else
sensitivityVEB = 0;
end
if(den2~=0)
specificityVEB = (num/den2) * 100;
else
specificityVEB = 0;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityVEB = -1;
specificityVEB = -1;
end
if(size(cm1,1)>=4)
t = 4;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
if(den1~=0)
sensitivityF = (num/den1) * 100;
else
sensitivityF = 0;
end
if(den2~=0)
specificityF = (num/den2) * 100;
else
specificityF = 0;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityF = -1;
specificityF = -1;
end
if(size(cm1,1)>=5)
t = 5;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
if(den1~=0)
sensitivityQ = (num/den1) * 100;
else
sensitivityQ = 0;
end
if(den2~=0)
specificityQ = (num/den2) * 100;
else
specificityQ = 0;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityQ = -1;
specificityQ = -1;
end
fprintf(arq,'%6d & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f \\\\ \n',...
str2double(registers(k)), 100*acc_num/acc_den, sensitivityN, sensitivitySVEB,sensitivityVEB, sensitivityF, sensitivityQ,...
specificityN, specificitySVEB,specificityVEB, specificityF, specificityQ);
fprintf(arq,'\n');
fprintf('\n');
fprintf('Registro=%6.0f | Acc=%6.2f |', str2double(registers(k)), 100*acc_num/acc_den);
fprintf(' N_Se=%6.2f SVEB_Se=%6.2f VEB_Se=%6.2f N+P=%6.2f SVEB+P=%6.2f VEB+P=%6.2f \n\n',...
sensitivityN, sensitivitySVEB,sensitivityVEB, specificityN, specificitySVEB,specificityVEB)
end % for t
fprintf(arq,' \\hline \n');
%fprintf(arq,' \\hline \n');
fprintf(arq, ' Gross & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f \\\\ \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3)+finalCM(4,4)+finalCM(5,5))/(finalCM(1,:)+finalCM(2,:)+finalCM(3,:)+finalCM(4,:)+finalCM(5,:5)) ,...
100*finalCM(1,1)/finalCM(1,:), 100*finalCM(2,2)/finalCM(2,:), 100*finalCM(3,3)/finalCM(3,:), 100*finalCM(4,4)/finalCM(4,:), 100*finalCM(5,5)/finalCM(5,:),...
100*finalCM(1,1)/finalCM(:,1), 100*finalCM(2,2)/finalCM(:,2), 100*finalCM(3,3)/finalCM(:,3), 100*finalCM(4,4)/finalCM(:,4), 100*finalCM(5,5)/finalCM(:,5));
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 & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f & %6.2f \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3)+finalCM(4,4)+finalCM(5,5))/(finalCM(1,:)+finalCM(2,:)+finalCM(3,:)+finalCM(4,:)+finalCM(5,:5)) ,...
100*finalCM(1,1)/finalCM(1,:), 100*finalCM(2,2)/finalCM(2,:), 100*finalCM(3,3)/finalCM(3,:), 100*finalCM(4,4)/finalCM(4,:), 100*finalCM(5,5)/finalCM(5,:),...
100*finalCM(1,1)/finalCM(:,1), 100*finalCM(2,2)/finalCM(:,2), 100*finalCM(3,3)/finalCM(:,3), 100*finalCM(4,4)/finalCM(:,4), 100*finalCM(5,5)/finalCM(:,5));
end