From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: 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
---
 sourcecodes/bnt-master/netlab3.3/mlpprior.m | 62 +++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 sourcecodes/bnt-master/netlab3.3/mlpprior.m

(limited to 'sourcecodes/bnt-master/netlab3.3/mlpprior.m')

diff --git a/sourcecodes/bnt-master/netlab3.3/mlpprior.m b/sourcecodes/bnt-master/netlab3.3/mlpprior.m
new file mode 100644
index 00000000..8f7415c5
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/mlpprior.m
@@ -0,0 +1,62 @@
+function prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)
+%MLPPRIOR Create Gaussian prior for mlp.
+%
+%	Description
+%	PRIOR = MLPPRIOR(NIN, NHIDDEN, NOUT, AW1, AB1, AW2, AB2)  generates a
+%	data structure PRIOR, with fields PRIOR.ALPHA and PRIOR.INDEX, which
+%	specifies a Gaussian prior distribution for the network weights in a
+%	two-layer feedforward network. Two different cases are possible. In
+%	the first case, AW1, AB1, AW2 and AB2 are all scalars and represent
+%	the regularization coefficients for four groups of parameters in the
+%	network corresponding to first-layer weights, first-layer biases,
+%	second-layer weights, and second-layer biases respectively. Then
+%	PRIOR.ALPHA represents a column vector of length 4 containing the
+%	parameters, and PRIOR.INDEX is a matrix specifying which weights
+%	belong in each group. Each column has one element for each weight in
+%	the matrix, using the standard ordering as defined in MLPPAK, and
+%	each element is 1 or 0 according to whether the weight is a member of
+%	the corresponding group or not.  In the second case the parameter AW1
+%	is a vector of length equal to the number of inputs in the network,
+%	and the corresponding matrix PRIOR.INDEX now partitions the first-
+%	layer weights into groups corresponding to the weights fanning out of
+%	each input unit. This  prior is appropriate for the technique of
+%	automatic relevance determination.
+%
+%	See also
+%	MLP, MLPERR, MLPGRAD, EVIDENCE
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+nextra = nhidden + (nhidden + 1)*nout;
+nwts = nin*nhidden + nextra;
+
+if size(aw1) == [1,1] 
+
+    indx = [ones(1, nin*nhidden), zeros(1, nextra)]';
+  
+elseif size(aw1) == [1, nin]
+  
+    indx = kron(ones(nhidden, 1), eye(nin));
+    indx = [indx; zeros(nextra, nin)];
+
+else
+  
+    error('Parameter aw1 of invalid dimensions');
+    
+end
+
+extra = zeros(nwts, 3);
+
+mark1 = nin*nhidden;
+mark2 = mark1 + nhidden;
+extra(mark1 + 1:mark2, 1) = ones(nhidden,1);
+mark3 = mark2 + nhidden*nout;
+extra(mark2 + 1:mark3, 2) = ones(nhidden*nout,1);
+mark4 = mark3 + nout;
+extra(mark3 + 1:mark4, 3) = ones(nout,1);
+
+indx = [indx, extra];
+
+prior.index = indx;
+prior.alpha = [aw1, ab1, aw2, ab2]';
-- 
cgit 1.4.1