%function exrankboost1(data);%  Model Selection for analysing rankboost performance %  on ROC curve optimization  with real data % % This code has been used in the SVM-AUC TechRep% 30/07/2004 A. Rakotomamonjyclear allclose alldata='credit';file=['../data/' data '/' data '.mat'];load(file);nbtrain=300;classcode=[1 -1];Tvec=[50 100];verbose=0;for i=1:20    fprintf('%d..',i);   [xapp,yapp,xtest,ytest,indice]=CreateDataAppTest(x,y,nbtrain, classcode);   [xapp,xtest] = normalizemeanstd(xapp,xtest);    for j=1:length(Tvec)         T=Tvec(j);        fprintf('-');        [alpha,threshold,rankfeat]=rankboostAUC(xapp,yapp,T);         ypred=rankboostAUCval(xapp,alpha,threshold,rankfeat,T);        [AUC,tpr,fpr,b]=rankroccurve(ypred,yapp);        ypredtest=rankboostAUCval(xtest,alpha,threshold,rankfeat,T);        [AUCtest,tpr,fpr]=rankroccurve(ypredtest,ytest);        [Conf,metric]=ConfusionMatrix(sign(ypredtest+b),ytest,classcode);        MAUCtest(j,i)=AUCtest;        Maccur(j,i)=metric.accuracy;        Mprecision(j,i)=metric.precision;        Mfmeasure(j,i)=metric.fmeasure;        Mwracc(j,i)=metric.wracc;        Mdetection(j,i)=metric.detection;    end;        end;mauctest=mean(MAUCtest,2);maccur=mean(Maccur,2);mprecision=mean(Mprecision,2);mfmeasure=mean(Mfmeasure,2);mwracc=mean(Mwracc,2);mdetection=mean(Mdetection,2);% sauctest=std(MAUCtest,0,2);% saccur=std(Maccur,0,2);% sprecision=std(Mprecision,0,3);% sfmeasure=std(Mfmeasure,0,3);% swracc=std(Mwracc,0,3);% sdetection=std(Mdetection,0,3);mauctest=mean(MAUCtest,2);maccur=mean(Maccur,2);%save([data 'rank.mat'])