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, 59 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/graph_to_jtree.m b/sourcecodes/bnt-master/graph/graph_to_jtree.m
new file mode 100644
index 00000000..952bbca7
--- /dev/null
+++ b/sourcecodes/bnt-master/graph/graph_to_jtree.m
@@ -0,0 +1,59 @@
+function [jtree, root, cliques, B, w, elim_order] = graph_to_jtree(MG, ns, partial_order, stages, clusters)
+% GRAPH_TO_JTREE Triangulate a graph and make a junction tree from its cliques.
+% [jtree, root, cliques, B, w, elim_order] = ...
+%    graph_to_jtree(graph, node_sizes, partial_order, stages, clusters)
+%
+% INPUT:
+% graph(i,j) = 1 iff there is an edge between i,j
+% node_weights(i) = num discrete values node i can take on [1 if observed]
+% partial_order = {} if no constraints on elimination ordering
+% stages{i} = nodes that must be eliminated at i'th stage (if porder is empty)
+% clusters{i} = list of nodes that must get connected together in the moral graph
+%
+% OUTPUT:
+% jtree(i,j) = 1 iff there is an arc between clique i and clique j 
+% root = the root clique
+% cliques{i} = the nodes in clique i
+% B(i,j) = 1 iff node j occurs in clique i
+% w(i) = weight of clique i
+
+N = length(MG);
+
+if nargin >= 5
+  % Add extra arcs between nodes in each cluster to ensure they occur in the same clique
+  for i=1:length(clusters)
+    c = clusters{i};
+    MG(c,c) = 1;
+  end
+end
+MG = setdiag(MG, 0);
+
+% Find an optimal elimination ordering (NP-hard problem!)
+if nargin < 4
+  stages = {1:N};
+end
+if nargin < 3
+  partial_order = {};
+end
+if isempty(partial_order)
+  strong = 0;
+  elim_order = best_first_elim_order(MG, ns, stages);
+else
+  strong = 1;
+  elim_order = strong_elim_order(MG, ns, partial_order);
+end
+
+[MTG, cliques, fill_in_edges]  = triangulate(MG, elim_order);
+
+% Connect the cliques up into a jtree,
+[jtree, root, B, w] = cliques_to_jtree(cliques, ns);
+
+if 0
+  disp('testing dag to jtree');
+  % Find the cliques containing each node, and check they form a connected subtree
+  clqs_con_node = cell(1,N);
+  for i=1:N
+    clqs_con_node{i} = find(B(:,i))';
+  end
+  check_jtree_property(clqs_con_node, jtree);
+end