p450bskel.m

一个关于adaboost和NN的matlab程序

% WBL 25 Sep 2002 Use P450 training data to train Matlab MLP Neural Network

versn = '$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 coding
tntests = size(tB,1);
validation.T = zeros(2,tntests);
for i=1:tntests
     validation.T(tB(i)+1.0,i) = 1.0;
end

for 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