%function exroccurvemodelsel(data);%% Model Selection script of SVM-L2 ROC curve maximization% with regards to different criterion such as AUC, Accuraccy% precision or Fmeas% 30/07/2004 A. Rakotomamonjyclear allclose alldata='wpbc';fprintf('ModelSelection\n');file=['../data/' data '/' data '.mat'];load(file);nbtrain=round(length(y)*0.90);classcode=[1 -1];kernel='gaussian';span=1;lambda = 1e-12;  Cvec=[0.1 0.5 1 5 10 50 100 500 1000];kerneloptionvec=[ 0.5 1 1.5 2 2.5 3 5 10 15];% Cvec=[0.1 0.5 ];%  kerneloptionvec=[0.5 0.75];verbose=0;for i=1:5    fprintf('%d..',i);    [Xapp,Yapp,xtest,ytest]=nfcv(x,y,5,i);    [Xapp,xtest] = normalizemeanstd(Xapp,xtest);    for m=1:5        [xapp,yapp,xval,yval]=nfcv(Xapp,Yapp,5,m);        for j=1:length(Cvec)             C=Cvec(j);            for k=1:length(kerneloptionvec)                kerneloption=kerneloptionvec(k);                [xsup,w,w0,pos,tps,alpha] = svmclassL2(xapp,yapp,C,lambda,kernel,kerneloption,verbose);                 [AUCval,tpr,fpr]=svmroccurve(xval,yval,xsup,w,w0,kernel,kerneloption,span);                [ypred]=svmval(xval,xsup,w,w0,kernel,kerneloption,span);                [Conf,metric]=ConfusionMatrix(sign(ypred),yval,classcode);                MAUC(j,k,m)=AUCval;                Maccur(j,k,m)=metric.accuracy;                Mprecision(j,k,m)=metric.precision;                Mfmeasure(j,k,m)=metric.fmeasure;                Mwracc(j,k,m)=metric.wracc;                Mdetection(j,k,m)=metric.detection;            end;        end;    end;    mauctest=mean(MAUC,3);    maccur=mean(Maccur,3);    mprecision=mean(Mprecision,3);    mfmeasure=mean(Mfmeasure,3);    mwracc=mean(Mwracc,3);    mdetection=mean(Mdetection,3);        % AUC    [aux,indj]=max(max(mauctest));    [aux,indi]=max(max(mauctest'));    C=Cvec(indi);    kerneloption=kerneloptionvec(indj);        [xsup,w,w0,pos,tps,alpha] = svmclassL2(Xapp,Yapp,C,lambda,kernel,kerneloption,verbose);     [AUCtest,tpr,fpr]=svmroccurve(xtest,ytest,xsup,w,w0,kernel,kerneloption,span);    [ypred]=svmval(xtest,xsup,w,w0,kernel,kerneloption,span);    [Conf,metric]=ConfusionMatrix(sign(ypred),ytest,classcode);    auctest(i)=AUCtest;    aucval(i)=aux;    % accur    [aux,indj]=max(max(maccur));    [aux,indi]=max(max(maccur'));    C=Cvec(indi);    kerneloption=kerneloptionvec(indj);    [xsup,w,w0,pos,tps,alpha] = svmclassL2(Xapp,Yapp,C,lambda,kernel,kerneloption,verbose);     [ypred]=svmval(xtest,xsup,w,w0,kernel,kerneloption,span);    [Conf,metric]=ConfusionMatrix(sign(ypred),ytest,classcode);    accur(i)=metric.accuracy;    accurval(i)=aux;         % precision    [aux,indj]=max(max(mprecision));    [aux,indi]=max(max(mprecision'));    C=Cvec(indi);    kerneloption=kerneloptionvec(indj);    [xsup,w,w0,pos,tps,alpha] = svmclassL2(Xapp,Yapp,C,lambda,kernel,kerneloption,verbose);     [ypred]=svmval(xtest,xsup,w,w0,kernel,kerneloption,span);    [Conf,metric]=ConfusionMatrix(sign(ypred),ytest,classcode);    prec(i)=metric.precision;    precval(i)=aux;         % fmeasure    [aux,indj]=max(max(mfmeasure));    [aux,indi]=max(max(mfmeasure'));    C=Cvec(indi);    kerneloption=kerneloptionvec(indj);    [xsup,w,w0,pos,tps,alpha] = svmclassL2(Xapp,Yapp,C,lambda,kernel,kerneloption,verbose);     [ypred]=svmval(xtest,xsup,w,w0,kernel,kerneloption,span);    [Conf,metric]=ConfusionMatrix(sign(ypred),ytest,classcode);    fmeas(i)=metric.fmeasure;    fmeasval(i)=aux;end;save([data 'modelsel.mat']);