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, 75 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/strong_elim_order.m b/sourcecodes/bnt-master/graph/strong_elim_order.m
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+++ b/sourcecodes/bnt-master/graph/strong_elim_order.m
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+function order = strong_elim_order(G, node_sizes, partial_order)
+% STRONG_ELIM_ORDER Find an elimination order to produce a strongly triangulated graph.
+% order = strong_elim_order(moral_graph, node_sizes, partial_order)
+% 
+% partial_order(i,j)=1 if we must marginalize i *after* j
+% (so i will be nearer the strong root).
+% e.g., if j is a decision node and i is its information set:
+%   we cannot maximize j if we have marginalized out some of i
+% e.g., if j is a continuous child and i is its discrete parent:
+%   we want to integrate out the cts nodes before the discrete ones,
+%   so that the marginal is strong.
+%
+% For details, see
+% - Jensen, Jensen and Dittmer, "From influence diagrams to junction trees", UAI 94.
+% - Lauritzen, "Propgation of probabilities, means, and variances in mixed graphical
+%   association models", JASA 87(420):1098--1108, 1992.
+%
+% On p369 of the Jensen paper, they state "the reverse of the elimination order must be some
+% extension of [the partial order] to a total order".
+% We make no attempt to find the best such total ordering, in the sense of minimizing the weight
+% of the resulting cliques.
+
+% Example from the Jensen paper:
+% Let us number the nodes in Fig 1 from top to bottom, left to right,
+% so a=1,b=2,D1=3,c=4,...,l=14,j=15,k=16.
+% The elimination ordering they propose on p370 is [14 15 16 11 12 1 4 5 10 8 13 9 7 6 3 2];
+
+if 0
+  total_order = topological_sort(partial_order);
+  order = total_order(end:-1:1); % no attempt to find an optimal constrained ordering!
+  return;
+end
+
+% The following implementation is due to Ilya Shpitser and seems to give wrong
+% results on cg1
+
+n = length(G);
+MG = G; % copy the original graph
+uneliminated = ones(1,n);
+order = zeros(1,n);
+
+for i=1:n
+  roots = [];
+  k = 1;
+  for j=1:n
+    if sum(partial_order(j,:)) == 0
+      roots(k) = j;
+      k = k + 1;
+    end
+  end
+  U = find(uneliminated);
+  valid = myintersect(U, roots);
+  % Choose the best node from the set of valid candidates
+  score1 = zeros(1,length(valid));
+  score2 = zeros(1,length(valid));
+  for j=1:length(valid)
+    k = valid(j);
+    ns = myintersect(neighbors(G, k), U);
+    l = length(ns);
+    M = MG(ns,ns);
+    score1(j) = l^2 - sum(M(:)); % num. added edges
+    score2(j) = prod(node_sizes([k ns])); % weight of clique
+  end
+  j1s = find(score1==min(score1));
+  j = j1s(argmin(score2(j1s)));
+  k = valid(j);
+  uneliminated(k) = 0;
+  order(i) = k;
+  ns = myintersect(neighbors(G, k), U);
+  if ~isempty(ns)
+    G(ns,ns) = 1;
+    G = setdiag(G,0);
+  end
+  partial_order(:,k) = 0;
+end