testDNN analysis 6

% SetLinearMapping: set the RBM associated to the linear mapping to the last layer
%
% dbn = SetLinearMapping( dbn, IN, OUT )
%
%
%Input parameters:
% dbn: the Deep Belief Nets (DBN) model
% IN: visible (input) variables, where # of row is number of data and # of col is # of visible (input) nodes
% OUT: teaching data, where # of row is number of data and # of col is # of hidden (output) nodes
%
%
%Output parameters:
% dbn: the set Deep Belief Nets (DBN) model
%
%
%Example:
% datanum = 1024;
% outputnum = 16;
% hiddennum = 8;
% inputnum = 4;
%
% inputdata = rand(datanum, inputnum);
% outputdata = rand(datanum, outputnum);
%
% dbn = randDBN([inputnum, hiddennum, outputnum]);
% dbn = pretrainDBN( dbn, inputdata );
% dbn = SetLinearMapping( dbn, inputdata, outputdata );
% dbn = trainDBN( dbn, inputdata, outputdata );
%
% estimate = v2h( dbn, inputdata );

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% Deep Neural Network:                                     %
%                                                          %
% Copyright (C) 2013 Masayuki Tanaka. All rights reserved. %
%                    mtanaka@ctrl.titech.ac.jp             %
%                                                          %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function dbn = SetLinearMapping( dbn, IN, OUT ) % 1000 x 32 , 1000 x 4
nrbm = numel(dbn.rbm);
if( nrbm > 1 ) % 3
    Hall = v2hall( dbn, IN ); % 3 x 1 Hall{1}->1000 x 16, 1000 x 8, 1000 x 4
    dbn.rbm{nrbm}.W = linearMapping( Hall{nrbm-1}, OUT ); % 1000 x 8, 1000 x 4 = 8 x 4
    dbn.rbm{nrbm}.b = -0.5 * ones(size(dbn.rbm{nrbm}.b)); % 1 x 4
else
    dbn.rbm{nrbm}.W = linearMapping( IN, OUT );
    dbn.rbm{nrbm}.b = -0.5 * ones(size(dbn.rbm{nrbm}.b));
end