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

5 files changed, 116 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Entries b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Entries
new file mode 100644
index 00000000..0b350b99
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Entries
@@ -0,0 +1,3 @@
+/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002//
+/marginal_nodes_SS.m/1.1.1.1/Wed May 29 15:59:56 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Repository b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Repository
new file mode 100644
index 00000000..59988183
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Root b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m
new file mode 100644
index 00000000..cd660ae4
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m
@@ -0,0 +1,59 @@
+function [m, pot] = marginal_family(engine, query)
+% MARGINAL_NODES Compute the marginal on the family of the specified node (jtree_limid)
+% [m, pot] = marginal_family(engine, query)
+%
+% query should be a single decision node, or [] (to compute global max expected utility)
+
+bnet = bnet_from_engine(engine);
+if isempty(query)
+  compute_meu = 1;
+  d = bnet.decision_nodes(1); % pick an arbitrary root to collect to
+  fam = []; % marginalize root pot down to a point
+else
+  compute_meu = 0;
+  d = query;
+  assert(myismember(d, bnet.decision_nodes));
+  fam = family(bnet.dag, d);
+end
+
+clpot = init_clpot(bnet, engine.cliques, engine.clq_ass_to_node, engine.evidence, engine.exclude);
+
+% collect to root (clique containing d) 
+C = length(engine.cliques);
+seppot = cell(C, C);    % separators are implicitely initialized to 1s
+for n=engine.postorder{d}(1:end-1)
+  for p=parents(engine.rooted_jtree{d}, n)
+    %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant
+    seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n});
+    clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n});
+  end
+end
+
+root = engine.clq_ass_to_node(d);
+assert(root == engine.postorder{d}(end));
+pot = marginalize_pot(clpot{root}, fam);
+m = pot_to_marginal(pot);
+
+%%%%%%%%%%%
+
+
+function clpot = init_clpot(bnet, cliques, clq_ass_to_node, evidence, exclude)
+
+% Set the clique potentials to all 1s
+C = length(cliques);
+clpot = cell(1, C);
+ns = bnet.node_sizes;
+for i=1:C
+  clpot{i} = upot(cliques{i}, ns(cliques{i}));
+end
+
+N = length(bnet.dag);
+nodes = mysetdiff(1:N, exclude);
+
+for n=nodes(:)'
+  fam = family(bnet.dag, n);
+  e = bnet.equiv_class(n);
+  c = clq_ass_to_node(n);
+  pot = convert_to_pot(bnet.CPD{e}, 'u', ns, fam, evidence);
+  clpot{c} = multiply_by_pot(clpot{c}, pot);
+end
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m
new file mode 100644
index 00000000..2b6ff642
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m
@@ -0,0 +1,52 @@
+function [pot, MEU] = marginal_nodes(engine, d)
+
+C = length(cliques);
+%clpot = init_clpot(limid, cliques, d, clq_ass_to_node);
+clpot = init_clpot(limid, cliques, [], clq_ass_to_node);
+
+% collect to root
+if 1
+  % HUGIN
+  seppot = cell(C, C);    % separators are implicitely initialized to 1s
+  for n=postorder{di}(1:end-1)
+    for p=parents(rooted_jtree{di}, n)
+      %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant
+      seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n});
+      clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n});
+    end
+  end
+else
+  % Shafer-Shenoy
+  msg = cell(C,C);
+  for n=postorder{di}(1:end-1)
+    for c=children(rooted_jtree{di}, n)
+      clpot{n} = multiply_by_pot(clpot{n}, msg{c,n});
+    end
+    p = parents(rooted_jtree{di}, n);
+    %msg{n,p} = marginalize_pot(clpot{n}, cliques{p});
+    msg{n,p} = marginalize_pot(clpot{n}, separator{n,p});
+  end
+  root = clq_ass_to_node(d);
+  n=postorder{di}(end);
+  assert(n == root);
+  for c=children(rooted_jtree{di}, n)
+    clpot{n} = multiply_by_pot(clpot{n}, msg{c,n});
+  end
+end	
+
+fam = family(limid.dag, d);
+pot = marginalize_pot(clpot{root}, fam);
+
+%%%%%%%
+jpot = compute_joint_pot_limid(limid);
+pot2 = marginalize_pot(jpot, fam);
+assert(approxeq_pot(pot, pot2))
+%%%%%%
+
+[policy, score] = extract_policy(pot);
+
+e = limid.equiv_class(d);
+limid.CPD{e} = set_params(limid.CPD{e}, 'policy', policy);
+
+  
+