goDT.m

% 
% /******************************************************************************
% 
% This program, "NCDM", the associated MATLAB scripts and all 
% provided data, are copyright (C) 2013-2014 Andrew R. Cohen and Paul
% M. B. Vitanyi.  All rights reserved.
% 
% This program uses bzip2 compressor as a static library.
% See the file SRC\C\bz2static\LICENSE.txt for details on that software.
% 
% This software may be referenced as:
% 
% A.R.Cohen and P.M.B. Vitanyi, "Normalized Compression Distance of Multisets 
% with Applications," IEEE Transactions on Pattern Analysis and Machine 
% Intelligence. 2014. In Press. Also arXiv:1212.5711.  
% 
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% modification, are permitted provided that the following conditions
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% 1. Redistributions of source code must retain the above copyright
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% 2. The origin of this software must not be misrepresented; you must 
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%    software in a product, an acknowledgment in the product 
%    documentation would be appreciated but is not required.
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% THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
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% Andrew R. Cohen acohen@coe.drexel.edu
% Paul M. B. Vitanyi Paul.Vitanyi@cwi.nl
% NCDM  version 1.0 of 13 March 2013
% NCDM  version 2.0 (release) November 2014
% 
% ******************************************************************************/

% goDT ensemble of random subspace discriminant classifiers

function res=goDT(rgTrain,rgTest)

trainData = rgTrain(:,2:end);
trainLabel = rgTrain(:,1);

testData = rgTest(:,2:end);
testLabel = rgTest(:,1);

[size(testData) size(trainData)]

res=[];
for nClass=[10 12 14]
    for npred=3000:1000:5000 %size(testData,2)
        tic
        ens = fitensemble(trainData,trainLabel,'subspace',nClass,'discriminant','NPredToSample',npred);
        pred=ens.predict(testData);
        accuracy=length(find(pred==testLabel))/length(testLabel);
%         pred=ens.predict(trainData);
%         cvAccuracy=length(find(pred==trainLabel))/length(trainLabel);
        cvEns = crossval(ens);
        cvAccuracy=1-kfoldLoss(cvEns);
        res=[res;nClass npred accuracy cvAccuracy];
        best = max(res(:,4));
        fprintf(1,'discriminant ensemble (%d,%d): accuracy=%.4f,cvAccuracy=%.4f best=%.4f\n',nClass,npred,accuracy,cvAccuracy,best);
        
        toc
        
    end
end