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, 28 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/general/score_bnet_complete.m b/sourcecodes/bnt-master/BNT/general/score_bnet_complete.m
new file mode 100644
index 00000000..93caf1d7
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/general/score_bnet_complete.m
@@ -0,0 +1,28 @@
+function L = log_lik_complete(bnet, cases, clamped)
+% LOG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m), theta_i) for a completely observed data set
+% L = log_lik_complete(bnet, cases, clamped)
+%
+% If there is a missing data, you must use an inference engine.
+% cases(i,m) is the value assigned to node i in case m.
+% (If there are vector-valued nodes, cases should be a cell array.)
+% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros)
+% Clamped nodes contribute a factor of 1.0 to the likelihood.
+
+if iscell(cases), usecell = 1; else usecell = 0; end
+
+n = length(bnet.dag);
+ncases = size(cases, 2);
+if n ~= size(cases, 1)
+  error('data should be of size nnodes * ncases');
+end
+
+if nargin < 3, clamped = zeros(n,ncases); end
+
+L = 0;
+for i=1:n
+  ps = parents(bnet.dag, i);
+  e = bnet.equiv_class(i);
+  u = find(clamped(i,:)==0);
+  L = L + log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u));
+end
+