BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 153 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gtminit.m b/sourcecodes/bnt-master/netlab3.3/gtminit.m
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+function net = gtminit(net, options, data, samp_type, varargin)
+%GTMINIT Initialise the weights and latent sample in a GTM.
+%
+%	Description
+%	NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and
+%	generates a sample of latent data points and sets the centres (and
+%	widths if appropriate) of NET.RBFNET.
+%
+%	If the SAMPTYPE is 'REGULAR', then regular grids of latent data
+%	points and RBF centres are created.  The dimension of the latent data
+%	space must be 1 or 2.  For one-dimensional latent space, the
+%	LSAMPSIZE parameter gives the number of latent points and the
+%	RBFSAMPSIZE parameter gives the number of RBF centres.  For a two-
+%	dimensional latent space, these parameters must be vectors of length
+%	2 with the number of points in each of the x and y directions to
+%	create a rectangular grid.  The widths of the RBF basis functions are
+%	set by a call to RBFSETFW passing OPTIONS(7) as the scaling
+%	parameter.
+%
+%	If the SAMPTYPE is 'UNIFORM' or 'GAUSSIAN' then the latent data is
+%	found by sampling from a uniform or Gaussian distribution
+%	correspondingly.  The RBF basis function parameters are set by a call
+%	to RBFSETBF with the DATA parameter as dataset and the OPTIONS
+%	vector.
+%
+%	Finally, the output layer weights of the RBF are initialised by
+%	mapping the mean of the latent variable to the mean of the target
+%	variable, and the L-dimensional latent variale variance to the
+%	variance of the targets along the first L principal components.
+%
+%	See also
+%	GTM, GTMEM, PCA, RBFSETBF, RBFSETFW
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check for consistency
+errstring = consist(net, 'gtm', data);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+% Check type of sample
+stypes = {'regular', 'uniform', 'gaussian'};
+if (strcmp(samp_type, stypes)) == 0
+  error('Undefined sample type.')
+end
+
+if net.dim_latent > size(data, 2)
+  error('Latent space dimension must not be greater than data dimension')
+end
+nlatent = net.gmmnet.ncentres;
+nhidden = net.rbfnet.nhidden;
+
+% Create latent data sample and set RBF centres
+
+switch samp_type
+case 'regular'
+   if nargin ~= 6
+      error('Regular type must specify latent and RBF shapes');
+   end
+   l_samp_size = varargin{1};
+   rbf_samp_size = varargin{2};
+   if round(l_samp_size) ~= l_samp_size
+      error('Latent sample specification must contain integers')
+   end
+   % Check existence and size of rbf specification
+   if any(size(rbf_samp_size) ~= [1 net.dim_latent]) | ...
+         prod(rbf_samp_size) ~= nhidden
+      error('Incorrect specification of RBF centres')
+   end
+   % Check dimension and type of latent data specification
+   if any(size(l_samp_size) ~= [1 net.dim_latent]) | ...
+         prod(l_samp_size) ~= nlatent
+      error('Incorrect dimension of latent sample spec.')
+   end
+   if net.dim_latent == 1
+      net.X = [-1:2/(l_samp_size-1):1]';
+      net.rbfnet.c = [-1:2/(rbf_samp_size-1):1]';
+      net.rbfnet = rbfsetfw(net.rbfnet, options(7));
+   elseif net.dim_latent == 2
+      net.X = gtm_rctg(l_samp_size);
+      net.rbfnet.c = gtm_rctg(rbf_samp_size);
+      net.rbfnet = rbfsetfw(net.rbfnet, options(7));
+   else
+      error('For regular sample, input dimension must be 1 or 2.')
+   end
+   
+   
+case {'uniform', 'gaussian'}
+   if strcmp(samp_type, 'uniform')
+      net.X = 2 * (rand(nlatent, net.dim_latent) - 0.5);
+   else
+      % Sample from N(0, 0.25) distribution to ensure most latent 
+      % data is inside square
+      net.X = randn(nlatent, net.dim_latent)/2;
+   end   
+   net.rbfnet = rbfsetbf(net.rbfnet, options, net.X);
+otherwise
+   % Shouldn't get here
+   error('Invalid sample type');
+   
+end
+
+% Latent data sample and basis function parameters chosen.
+% Now set output weights
+[PCcoeff, PCvec] = pca(data);
+
+% Scale PCs by eigenvalues
+A = PCvec(:, 1:net.dim_latent)*diag(sqrt(PCcoeff(1:net.dim_latent)));
+
+[temp, Phi] = rbffwd(net.rbfnet, net.X);
+% Normalise X to ensure 1:1 mapping of variances and calculate weights
+% as solution of Phi*W = normX*A'
+normX = (net.X - ones(size(net.X))*diag(mean(net.X)))*diag(1./std(net.X));
+net.rbfnet.w2 = Phi \ (normX*A');
+% Bias is mean of target data
+net.rbfnet.b2 = mean(data);
+
+% Must also set initial value of variance
+% Find average distance between nearest centres
+% Ensure that distance of centre to itself is excluded by setting diagonal
+% entries to realmax
+net.gmmnet.centres = rbffwd(net.rbfnet, net.X);
+d = dist2(net.gmmnet.centres, net.gmmnet.centres) + ...
+  diag(ones(net.gmmnet.ncentres, 1)*realmax);
+sigma = mean(min(d))/2;
+
+% Now set covariance to minimum of this and next largest eigenvalue
+if net.dim_latent < size(data, 2)
+  sigma = min(sigma, PCcoeff(net.dim_latent+1));
+end
+net.gmmnet.covars = sigma*ones(1, net.gmmnet.ncentres);
+
+% Sub-function to create the sample data in 2d
+function sample = gtm_rctg(samp_size)
+
+xDim = samp_size(1);
+yDim = samp_size(2);
+% Produce a grid with the right number of rows and columns
+[X, Y] = meshgrid([0:1:(xDim-1)], [(yDim-1):-1:0]);
+
+% Change grid representation 
+sample = [X(:), Y(:)];
+
+% Shift grid to correct position and scale it
+maxXY= max(sample);
+sample(:,1) = 2*(sample(:,1) - maxXY(1)/2)./maxXY(1);
+sample(:,2) = 2*(sample(:,2) - maxXY(2)/2)./maxXY(2);
+return;
+
+   
+