comparison toolboxes/FullBNT-1.0.7/graph/Old/best_first_elim_order.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function order = best_first_elim_order(G, node_sizes, stage)
2 % BEST_FIRST_ELIM_ORDER Greedily search for an optimal elimination order.
3 % order = best_first_elim_order(moral_graph, node_sizes)
4 %
5 % Find an order in which to eliminate nodes from the graph in such a way as to try and minimize the
6 % weight of the resulting triangulated graph. The weight of a graph is the sum of the weights of each
7 % of its cliques; the weight of a clique is the product of the weights of each of its members; the
8 % weight of a node is the number of values it can take on.
9 %
10 % Since this is an NP-hard problem, we use the following greedy heuristic:
11 % at each step, eliminate that node which will result in the addition of the least
12 % number of fill-in edges, breaking ties by choosing the node that induces the lighest clique.
13 % For details, see
14 % - Kjaerulff, "Triangulation of graphs -- algorithms giving small total state space",
15 % Univ. Aalborg tech report, 1990 (www.cs.auc.dk/~uk)
16 % - C. Huang and A. Darwiche, "Inference in Belief Networks: A procedural guide",
17 % Intl. J. Approx. Reasoning, 11, 1994
18 %
19
20 % Warning: This code is pretty old and could probably be made faster.
21
22 n = length(G);
23 if nargin < 3, stage = { 1:n }; end % no constraints
24
25 % For long DBNs, it may be useful to eliminate all the nodes in slice t before slice t+1.
26 % This will ensure that the jtree has a repeating structure (at least away from both edges).
27 % This is why we have stages.
28 % See the discussion of splicing jtrees on p68 of
29 % Geoff Zweig's PhD thesis, Dept. Comp. Sci., UC Berkeley, 1998.
30 % This constraint can increase the clique size significantly.
31
32 MG = G; % copy the original graph
33 uneliminated = ones(1,n);
34 order = zeros(1,n);
35 t = 1; % Counts which time slice we are on
36 for i=1:n
37 U = find(uneliminated);
38 valid = myintersect(U, stage{t});
39 % Choose the best node from the set of valid candidates
40 score1 = zeros(1,length(valid));
41 score2 = zeros(1,length(valid));
42 for j=1:length(valid)
43 k = valid(j);
44 ns = myintersect(neighbors(G, k), U);
45 l = length(ns);
46 M = MG(ns,ns);
47 score1(j) = l^2 - sum(M(:)); % num. added edges
48 score2(j) = prod(node_sizes([k ns])); % weight of clique
49 end
50 j1s = find(score1==min(score1));
51 j = j1s(argmin(score2(j1s)));
52 k = valid(j);
53 uneliminated(k) = 0;
54 order(i) = k;
55 ns = myintersect(neighbors(G, k), U);
56 if ~isempty(ns)
57 G(ns,ns) = 1;
58 G = setdiag(G,0);
59 end
60 if ~any(logical(uneliminated(stage{t}))) % are we allowed to the next slice?
61 t = t + 1;
62 end
63 end
64