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
---
 .../bnt-master/BNT/CPDs/@gmux_CPD/convert_to_pot.m | 37 ++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/convert_to_pot.m

(limited to 'sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/convert_to_pot.m')

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/convert_to_pot.m b/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/convert_to_pot.m
new file mode 100644
index 00000000..bf8c29c4
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/convert_to_pot.m
@@ -0,0 +1,37 @@
+function pot = convert_to_pot(CPD, pot_type, domain, evidence)
+% CONVERT_TO_POT Convert a gmux CPD to a Gaussian potential
+% pot = convert_to_pot(CPD, pot_type, domain, evidence)
+  
+switch pot_type
+ case {'d', 'u', 'cg', 'scg'},
+  error(['can''t convert gmux to potential of type ' pot_type])
+
+ case {'c','g'},
+  % We create a large weight matrix with zeros in all blocks corresponding
+  % to the non-chosen parents, since they are effectively disconnected.
+  % The chosen parent is determined by the value, m,  of the discrete parent.
+  % Thus the potential is as large as the whole family.
+  ps = domain(1:end-1);
+  dps = ps(CPD.dps); % CPD.dps is an index, not a node number (because of param tying)
+  cps = ps(CPD.cps);
+  m = evidence{dps};
+  if isempty(m)
+    error('gmux node must have observed discrete parent')
+  end
+  bs = CPD.sizes(CPD.cps);
+  b = block(m, bs);
+  sum_cpsz = sum(CPD.sizes(CPD.cps));
+  selfsz = CPD.sizes(end);
+  W = zeros(selfsz, sum_cpsz);
+  W(:,b) = CPD.weights(:,:,m);
+
+  ns = zeros(1, max(domain));
+  ns(domain) = CPD.sizes;
+  self = domain(end);
+  cdom = [cps(:)' self];
+  pot = linear_gaussian_to_cpot(CPD.mean(:,m), CPD.cov(:,:,m), W, domain, ns, cdom, evidence);
+  
+ otherwise,
+  error(['unrecognized pot_type' pot_type])
+end
+
-- 
cgit 1.4.1