% WBL 25 Sep 2002 Use P450 training data to train Matlab MLP Neural Networkversn = '$Revision: 1.7 $';  %$Date: 2002/10/31 11:17:37 $%read training data, one training case per line, %each case has the same number of attributes,%numbers are separated by spaces.in = load('TRAIN+11_FULLtrn');     in = in';%read training class. Each line has class of training case in the same line %just loaded by previous line. Classes 0 and 1 are supported.B  = load('TRAIN+11_FULLtrn.ans'); B  = B';%read validation data. Files have same format as training data.tin = load('VER+11_FULLtrn');     validation.P = tin';tB  = load('VER+11_FULLtrn.ans'); S1 = 2; %two hidden layers%expand scalar coding to two output neuron codingtntests = size(tB,1);validation.T = zeros(2,tntests);for i=1:tntests     validation.T(tB(i)+1.0,i) = 1.0;endfor i=1:1%10%50 %loop to run a number of experiments    filename = sprintf('13-oct-2002_%d.log', i);    outfile = fopen(filename,'w');    [betaT,net] = boost(in,B,validation,tB,outfile,5,S1);    filename = sprintf('13-oct-2002_%d', i);    save(filename,'versn','betaT','net');    fclose (outfile);    filename = sprintf('13-oct-2002_%d.dat', i);    outfile = fopen(filename,'w');    printroc(in,B,validation,tB,outfile,betaT,net,versn);    fclose (outfile);end