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

6 files changed, 68 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Entries b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Entries
new file mode 100644
index 00000000..e4399482
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Entries
@@ -0,0 +1,4 @@
+/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002//
+/enumerative_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002//
+/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Repository b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Repository
new file mode 100644
index 00000000..ee8672a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/inference/static/@enumerative_inf_engine
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Root b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/enter_evidence.m b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/enter_evidence.m
new file mode 100644
index 00000000..eeb2193c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/enter_evidence.m
@@ -0,0 +1,10 @@
+function [engine, loglik] = enter_evidence(engine, evidence)
+% ENTER_EVIDENCE Add the specified evidence to the network (enumerative_inf)
+% [engine, loglik] = enter_evidence(engine, evidence)
+%
+% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector)
+
+engine.evidence = evidence;
+if nargout == 2
+  [m, loglik] = marginal_nodes(engine, []);
+end
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/enumerative_inf_engine.m b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/enumerative_inf_engine.m
new file mode 100644
index 00000000..c31c64c9
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/enumerative_inf_engine.m
@@ -0,0 +1,11 @@
+function engine = enumerative_inf_engine(bnet)
+% ENUMERATIVE_INF_ENGINE Inference engine for fully discrete BNs that uses exhaustive enumeration.
+% engine = enumerative_inf_engine(bnet)
+
+
+assert(isempty(bnet.cnodes));
+
+% This is where we store stuff between enter_evidence and marginal_nodes
+engine.evidence = [];
+
+engine = class(engine, 'enumerative_inf_engine', inf_engine(bnet));
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/marginal_nodes.m b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/marginal_nodes.m
new file mode 100644
index 00000000..1c31eae1
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@enumerative_inf_engine/marginal_nodes.m
@@ -0,0 +1,41 @@
+function [marginal, loglik] = marginal_nodes(engine, query)
+% MARGINAL_NODES Compute the marginal on the specified query nodes (enumerative_inf)
+% [marginal, loglik] = marginal_nodes(engine, query)
+
+
+if isempty(query) & nargout < 2
+  marginal.T = 1;
+  marginal.domain = [];
+  return;
+end
+
+evidence = engine.evidence;
+bnet = bnet_from_engine(engine);
+assert(isempty(bnet.cnodes));
+n = length(bnet.dag);
+observed = ~isemptycell(evidence);
+vals = cat(1,evidence{observed});
+vals = vals(:)';
+ns = bnet.node_sizes;
+
+sz = ns(query);
+T = 0*myones(sz);
+p = 0;
+for i=1:prod(ns)
+  inst = ind2subv(ns, i); % i'th instantiation
+  if isempty(vals) | inst(observed) == vals % agrees with evidence
+    prob = exp(log_lik_complete(bnet, num2cell(inst(:))));
+    p = p + prob;
+    v = inst(query);
+    j = subv2ind(sz, v);
+    T(j) = T(j) + prob;
+  end
+end
+
+[T, lik] = normalise(T);
+lik = p;
+loglik = log(lik);
+
+Tsmall = shrink_obs_dims_in_table(T, query, evidence);
+marginal.domain = query;
+marginal.T = Tsmall;