function printroc(in,B,validation,tB,outfile,betaT,net,versn)% WBL 25 Sep 2002 Calculate and save ROC of boosted Matlab MLP Neural Network%version = '$Revision: 1.6 $';  %$Date: 2002/10/31 11:20:41 $%WBL 31 Oct 2002 Based on printroc2.m r1.5%WBL 25 Sep 2002 Based on printroc.m r1.1fprintf(outfile,'#P450bskel.m "%s" train %d, ver %d %s\n',versn,size(B,2),size(tB,1),datestr(now));tmax=size(net,2)for t=1:tmax    % use two output neurons.    %S1 = 2;    % expand B into targets. Each element becomes 2 element row,    % element coresponding to B(i) is one all others are zero.    %S2 = 2;         fprintf(outfile,'\n#%3d ',t);    net_tr = sim(net{t},in);    [area,roc]=errors(net_tr,B,outfile);    fprintf(outfile,'%f train\n',area);    dumproc(t,roc,outfile);        fprintf(outfile,'\n#%3d.1 ',t);    net_tr = sim(net{t},validation.P);    [area,roc]=errors(net_tr,tB',outfile);    fprintf(outfile,'%f ver\n',area);    dumproc(t+0.1,roc,outfile);end    fprintf(outfile,'\n#%3d ',-1);[area,net_tr,roc] = ensemble(tmax,betaT,net,in,          B,  outfile);fprintf(outfile,'ensemble of %d train AUROC %f\n',tmax,area);dumproc(-1,roc,outfile);    fprintf(outfile,'\n#%3d ',-1.1);[area,net_vr,roc] = ensemble(tmax,betaT,net,validation.P,tB',outfile);fprintf(outfile,'ensemble of %d ver AUROC %f\n',tmax,area);dumproc(-1.1,roc,outfile);%fclose (outfile);