testDNN analysis2

% randDBN: get randomized Deep Belief Nets (DBN) model
%
% dbn = randDBN( dims, type )
%
%
%Output parameters:
% dbn: the randomized Deep Belief Nets (DBN) model
%
%
%Input parameters:
% dims: number of nodes
% type (optional): (default: 'BBDBN' )
%                 'BBDBN': all RBMs are the Bernoulli-Bernoulli RBMs
%                 'GBDBN': the input RBM is the Gaussian-Bernoulli RBM, other RBMs are the Bernoulli-Bernoulli RBMs
%
%
%Version: 20130727


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Deep Neural Network:                                     %
%                                                          %
% Copyright (C) 2013 Masayuki Tanaka. All rights reserved. %
%                    mtanaka@ctrl.titech.ac.jp             %
%                                                          %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function dbn = randDBN( dims, type )

if( ~exist('type', 'var') || isempty(type) )
type = 'BBDBN';
end


if( strcmpi( 'GB', type(1:2) ) ) %Gaussian-Binary
 dbn.type = 'GBDBN';
else
 dbn.type = 'BBDBN';
end

dbn.rbm = cell( numel(dims)-1, 1 );

i = 1;
if( strcmpi( 'GB', type(1:2) ) )
 dbn.rbm{i} = randRBM( dims(i), dims(i+1), 'GBRBM' ); % Gaussian-Binary Restricted Bolztmann Machine
else
 dbn.rbm{i} = randRBM( dims(i), dims(i+1), 'BBRBM' ); % Bernoulli-Bernoulli Restricted Bolztmann Machine
end

for i=2:numel(dbn.rbm) - 1
 dbn.rbm{i} = randRBM( dims(i), dims(i+1), 'BBRBM' );
end

i = numel(dbn.rbm);
dbn.rbm{i} = randRBM( dims(i), dims(i+1), 'BBRBM' );