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, 123 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/rbf.m b/sourcecodes/bnt-master/netlab3.3/rbf.m
new file mode 100644
index 00000000..5c3b3096
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+++ b/sourcecodes/bnt-master/netlab3.3/rbf.m
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+function net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta)
+%RBF	Creates an RBF network with specified architecture
+%
+%	Description
+%	NET = RBF(NIN, NHIDDEN, NOUT, RBFUNC) constructs and initialises a
+%	radial basis function network returning a data structure NET. The
+%	weights are all initialised with a zero mean, unit variance normal
+%	distribution, with the exception of the variances, which are set to
+%	one. This makes use of the Matlab function RANDN and so the seed for
+%	the random weight initialization can be  set using RANDN('STATE', S)
+%	where S is the seed value. The activation functions are defined in
+%	terms of the distance between the data point and the corresponding
+%	centre.  Note that the functions are computed to a convenient
+%	constant multiple: for example, the Gaussian is not normalised.
+%	(Normalisation is not needed as the function outputs are linearly
+%	combined in the next layer.)
+%
+%	The fields in NET are
+%	  type = 'rbf'
+%	  nin = number of inputs
+%	  nhidden = number of hidden units
+%	  nout = number of outputs
+%	  nwts = total number of weights and biases
+%	  actfn = string defining hidden unit activation function:
+%	    'gaussian' for a radially symmetric Gaussian function.
+%	    'tps' for r^2 log r, the thin plate spline function.
+%	    'r4logr' for r^4 log r.
+%	  outfn = string defining output error function:
+%	    'linear' for linear outputs (default) and SoS error.
+%	    'neuroscale' for Sammon stress measure.
+%	  c = centres
+%	  wi = squared widths (null for rlogr and tps)
+%	  w2 = second layer weight matrix
+%	  b2 = second layer bias vector
+%
+%	NET = RBF(NIN, NHIDDEN, NOUT, RBFUND, OUTFUNC) allows the user to
+%	specify the type of error function to be used.  The field OUTFN is
+%	set to the value of this string.  Linear outputs (for regression
+%	problems) and Neuroscale outputs (for topographic mappings) are
+%	supported.
+%
+%	NET = RBF(NIN, NHIDDEN, NOUT, RBFUNC, OUTFUNC, PRIOR, BETA), in which
+%	PRIOR is a scalar, allows the field NET.ALPHA in the data structure
+%	NET to be set, corresponding to a zero-mean isotropic Gaussian prior
+%	with inverse variance with value PRIOR. Alternatively, PRIOR can
+%	consist of a data structure with fields ALPHA and INDEX, allowing
+%	individual Gaussian priors to be set over groups of weights in the
+%	network. Here ALPHA is a column vector in which each element
+%	corresponds to a separate group of weights, which need not be
+%	mutually exclusive.  The membership of the groups is defined by the
+%	matrix INDX in which the columns correspond to the elements of ALPHA.
+%	Each column has one element for each weight in the matrix, in the
+%	order defined by the function RBFPAK, and each element is 1 or 0
+%	according to whether the weight is a member of the corresponding
+%	group or not. A utility function RBFPRIOR is provided to help in
+%	setting up the PRIOR data structure.
+%
+%	NET = RBF(NIN, NHIDDEN, NOUT, FUNC, PRIOR, BETA) also sets the
+%	additional field NET.BETA in the data structure NET, where beta
+%	corresponds to the inverse noise variance.
+%
+%	See also
+%	RBFERR, RBFFWD, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+net.type = 'rbf';
+net.nin = nin;
+net.nhidden = nhidden;
+net.nout = nout;
+
+% Check that function is an allowed type
+actfns = {'gaussian', 'tps', 'r4logr'};
+outfns = {'linear', 'neuroscale'};
+if (strcmp(rbfunc, actfns)) == 0
+  error('Undefined activation function.')
+else
+  net.actfn = rbfunc;
+end
+if nargin <= 4
+   net.outfn = outfns{1};
+elseif (strcmp(outfunc, outfns) == 0)
+   error('Undefined output function.')
+else
+   net.outfn = outfunc;
+ end
+
+% Assume each function has a centre and a single width parameter, and that
+% hidden layer to output weights include a bias.  Only the Gaussian function
+% requires a width
+net.nwts = nin*nhidden + (nhidden + 1)*nout;
+if strcmp(rbfunc, 'gaussian')
+  % Extra weights for width parameters
+  net.nwts = net.nwts + nhidden;
+end
+
+if nargin > 5
+  if isstruct(prior)
+    net.alpha = prior.alpha;
+    net.index = prior.index;
+  elseif size(prior) == [1 1]
+    net.alpha = prior;
+  else
+    error('prior must be a scalar or a structure');
+  end  
+  if nargin > 6
+    net.beta = beta;
+  end
+end
+
+w = randn(1, net.nwts);
+net = rbfunpak(net, w);
+
+% Make widths equal to one
+if strcmp(rbfunc, 'gaussian')
+  net.wi = ones(1, nhidden);
+end
+
+if strcmp(net.outfn, 'neuroscale')
+  net.mask = rbfprior(rbfunc, nin, nhidden, nout);
+end
+