56 lines
2.9 KiB
Mathematica
56 lines
2.9 KiB
Mathematica
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function results = ensemble_testing(X,trained_ensemble)
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% -------------------------------------------------------------------------
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% Ensemble Classification | June 2013 | version 2.0 | TESTING ROUTINE
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% -------------------------------------------------------------------------
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% INPUT:
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% - X - testing features (in a row-by-row manner)
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% - trained_ensemble - trained ensemble - cell array of individual base
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% learners (output of the 'ensemble_training' routine)
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% OUTPUT:
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% - results.predictions - individual cover (-1) and stego (+1) predictions
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% based on the majority voting scheme
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% - results.votes - sum of all votes (gives some information about
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% prediction confidence)
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% -------------------------------------------------------------------------
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% Please see the main routine 'ensemble_training' for more information.
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% -------------------------------------------------------------------------
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% Copyright (c) 2013 DDE Lab, Binghamton University, NY.
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% All Rights Reserved.
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% -------------------------------------------------------------------------
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% Permission to use, copy, modify, and distribute this software for
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% educational, research and non-profit purposes, without fee, and without a
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% written agreement is hereby granted, provided that this copyright notice
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% appears in all copies. The program is supplied "as is," without any
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% accompanying services from DDE Lab. DDE Lab does not warrant the
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% operation of the program will be uninterrupted or error-free. The
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% end-user understands that the program was developed for research purposes
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% and is advised not to rely exclusively on the program for any reason. In
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% no event shall Binghamton University or DDE Lab be liable to any party
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% for direct, indirect, special, incidental, or consequential damages,
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% including lost profits, arising out of the use of this software. DDE Lab
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% disclaims any warranties, and has no obligations to provide maintenance,
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% support, updates, enhancements or modifications.
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% -------------------------------------------------------------------------
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% Contact: jan@kodovsky.com | fridrich@binghamton.edu | June 2013
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% http://dde.binghamton.edu/download/ensemble
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% -------------------------------------------------------------------------
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% References:
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% [1] - J. Kodovsky, J. Fridrich, and V. Holub. Ensemble classifiers for
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% steganalysis of digital media. IEEE Transactions on Information Forensics
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% and Security. Currently under review.
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% -------------------------------------------------------------------------
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% simple majority voting scheme
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votes = zeros(size(X,1),1);
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for i = 1:length(trained_ensemble)
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proj = X(:,trained_ensemble{i}.subspace)*trained_ensemble{i}.w-trained_ensemble{i}.b;
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votes = votes+sign(proj);
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end
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% resolve ties randomly
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votes(votes==0) = rand(sum(votes==0),1)-0.5;
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% form final predictions
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results.predictions = sign(votes);
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% output also the sum of the individual votes (~confidence info)
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results.votes = votes;
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