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, 57 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/cliques_to_jtree.m b/sourcecodes/bnt-master/graph/cliques_to_jtree.m
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+++ b/sourcecodes/bnt-master/graph/cliques_to_jtree.m
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+function [jtree, root, B, w] = cliques_to_jtree(cliques, ns)
+% MK_JTREE Make an optimal junction tree.
+% [jtree, root, B, w] = mk_jtree(cliques, ns)
+% 
+% A junction tree is a tree that satisfies the jtree property, which says:
+% for each pair of cliques U,V with intersection S, all cliques on the path between U and V
+% contain S. (This ensures that local propagation leads to global consistency.)
+%
+% We can create a junction tree by computing the maximal spanning tree of the junction graph.
+% (The junction graph connects all cliques, and the weight of an edge (i,j) is
+% |C(i) intersect C(j)|, where C(i) is the i'th clique.)
+%
+% The best jtree is the maximal spanning tree which minimizes the sum of the costs on each edge,
+% where cost(i,j) = w(C(i)) + w(C(j)), and w(C) is the weight of clique C,
+% which is the total number of values C can take on.
+%
+% For details, see
+%  - Jensen and Jensen, "Optimal Junction Trees", UAI 94.
+%
+% Input:
+%  cliques{i} = nodes in clique i
+%  ns(i) = number of values node i can take on
+% Output:
+%  jtree(i,j) = 1 iff cliques i and j aer connected
+%  root = the clique that should be used as root
+%  B(i,j) = 1 iff node j occurs in clique i
+%  w(i) = weight of clique i
+
+
+
+num_cliques = length(cliques);
+w = zeros(num_cliques, 1); 
+B = sparse(num_cliques, 1);
+for i=1:num_cliques
+  B(i, cliques{i}) = 1;
+  w(i) = prod(ns(cliques{i}));
+end
+
+
+% C1(i,j) = length(intersect(cliques{i}, cliques{j})); 
+% The length of the intersection of two sets is the dot product of their bit vector representation.
+C1 = B*B';
+C1 = setdiag(C1, 0);
+
+% C2(i,j) = w(i) + w(j)
+num_cliques = length(w);
+W = repmat(w, 1, num_cliques);
+C2 = W + W';
+C2 = setdiag(C2, 0);
+
+jtree = sparse(minimum_spanning_tree(-C1, C2)); % Using -C1 gives *maximum* spanning tree
+
+% The root is arbitrary, but since the first pass is towards the root,
+% we would like this to correspond to going forward in time in a DBN.
+root = num_cliques;
+
+