diff toolboxes/FullBNT-1.0.7/graph/strong_elim_order.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/graph/strong_elim_order.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,75 @@
+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