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, 20 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/inference/dynamic/@ff_inf_engine/marginal_nodes.m b/sourcecodes/bnt-master/BNT/inference/dynamic/@ff_inf_engine/marginal_nodes.m
new file mode 100644
index 00000000..f65a3bec
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/dynamic/@ff_inf_engine/marginal_nodes.m
@@ -0,0 +1,20 @@
+function marginal = marginal_nodes(engine, nodes, t)
+% MARGINAL_NODES Compute the marginal on the specified query nodes (ff)
+% marginal = marginal_nodes(engine, i, t)
+% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice.
+% If enter_evidence used filtering instead of smoothing, this will return  Pr(X(i,t) | Y(1:t)).
+
+if nargin < 3, t = 1; end
+assert(length(nodes)==1);
+i = nodes(end);
+if myismember(i, engine.hnodes)
+  marginal = pot_to_marginal(engine.marginals{i,t});
+else
+  marginal = pot_to_marginal(dpot(i, 1, 1)); % observed
+end
+
+bnet = bnet_from_engine(engine);
+ss = length(bnet.intra);
+% we convert the domain to the unrolled numbering system
+% so that update_ess extracts the right evidence.
+marginal.domain = nodes+(t-1)*ss;