function [betaT,net]=boost(in,B,validation,tB,outfile,bmax,S1)% WBL 22 August 2002 Boost Matlab MLP Neural Network bmax times%                          1 input neuron per attribute, size(in)%                          1 hidden layer of S1 inputs%                          2 output neurons versn = '$Revision: 1.10 $';  %$Date: 2002/10/31 11:14:02 $%Implement Pseudo-Loss ADAboost.M2 Table 1 p1873%"R" p1874, Boosting Neural Networks, %Schwenk and Bengio, Neural computation, 12 1869-1887(2000)%but use MATLAB to decide number of training iterations%(hence variable rather than fixed)%Do NOT use fancy cost function, just use Matlab default (least squares)%Resampled tests automatically given higher impact on cost function because they%occur multiple times in test set.%Only tests that appear in testset are updated (possibly multiple times)%others retain their previous value.%WBL 31 Oct 2002 based on P450boost2.m r1.9%WBL 30 Sep 2002 Update in line with P450boost.m r1.7%WBL 21 Sep 2002 Based on P450boost.m r1.6%WBL 21 Sep 2002 Based on P450train.m r1.1%WBL  9 Sep 2002 Based on read.m Try again but save just net not whole of Matlab%WBL 27 Aug 2002 Try hidden layer form 1..15 nodes, instead of just 20% expand B into targets. Each element becomes 2 element row,% element corresponding to B(i) is one all others are zero.% (already done for validation)ntests = size(B,2);S2 = 2; %number of output neuronstargets = zeros(S2,ntests);for i=1:ntests    targets(B(i)+1.0,i) = 1.0;end%weights used to decide which training data to use for boosted network%(data split into use for training and use for deciding when to stop training)weights = zeros(1,ntests);for i=1:ntests    weights(i) = 1/ntests;end%outfile = fopen('boost25-sep-2002.log','w');fprintf(outfile,'#boost.m %s train %d, ver %d %s\n',versn,ntests,size(tB,2),datestr(now));%bmax=10;betaT=zeros(1,bmax);t=1;for b=1:bmax    %Select training data for network using weights    if(t>1)        [index1,index2] = randselect(weights);    else         %first time use all training data         index1=1:1:ceil(ntests/2); %test=in; y=B;        index2=ceil(ntests/2)+1:1:ntests;    end    %gather statistics about data actually used for training    [t1,t1p,t1n,u1,u1p,u1n] = numtests(targets,index1);    n=hist(index1,[1:ntests]);    fprintf(         '%d %s Range test(%d,%d,%d,%d,%d,%d) %d %d %d ',b,datestr(now),t1,t1p,t1n,u1,u1p,u1n,min(n),max(n),median(n));    fprintf(outfile,'%4d %s Range test(%d,%d,%d,%d,%d,%d) %d %d %d ',b,datestr(now),t1,t1p,t1n,u1,u1p,u1n,min(n),max(n),median(n));    [t2,t2p,t2n,u2,u2p,u2n] = numtests(targets,index2);    n=hist(index2,[1:ntests]);    fprintf(        'stop(%d,%d,%d,%d,%d,%d) %d %d %d ',t2,t2p,t2n,u2,u2p,u2n,min(n),max(n),median(n));    fprintf(outfile,'stop(%d,%d,%d,%d,%d,%d) %d %d %d ',t2,t2p,t2n,u2,u2p,u2n,min(n),max(n),median(n));        train     =      in(:,index1);    trainans  = targets(:,index1);    stopset.P =      in(:,index2);    stopset.T = targets(:,index2);    [net{t},epochs,rms]=trainnet(train,trainans,S1,stopset);    net_tr = sim(net{t},in);    %net_tr = simple(targets);    [betaT(t),loss] = pseudoloss(weights,net_tr,targets);    fprintf(        'epochs %3d RMS %f beta %f\n',epochs,rms,betaT(t));    fprintf(outfile,'epochs %3d RMS %f beta %f ', epochs,rms,betaT(t));    %NON STANDARD paper says stop if beta>1 but get such large stochastic effects on P450 data    %would stop many time on first trail! Hence simply discard this trial - roll back and try again    if(betaT(t)>=1)         fprintf(outfile,'Boosting trial %4d failed\n',b);    else         [roc,net_tr] = ensemble(t,betaT,net,in,          B,  outfile);        fprintf(outfile,'AUROC %f ',roc);        [roc,net_vr] = ensemble(t,betaT,net,validation.P,tB',outfile);        fprintf(outfile,'AUROC %f\n',roc);                weights=update(weights,betaT(t),loss);        t = t+1;        fprintf(outfile,'#%4d %4d Range of weights min %f (%f) max %f (%f)\n',b,t,...            min(weights), min(weights)/mean(weights),...            max(weights), max(weights)/mean(weights));    endend%save('boost','versn','betaT','net');%fclose (outfile);