Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/general/fgraph_to_bnet.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function bnet = fgraph_to_bnet(fg) | |
2 % FGRAPH_TO_BNET Convert a factor graph to a Bayes net | |
3 % bnet = fgraph_to_bnet(fg) | |
4 % | |
5 % We assume all factors are tabular_CPD. | |
6 % We create 1 dummy observed node for every factor. | |
7 | |
8 N = fg.nvars + fg.nfactors; | |
9 vnodes = 1:fg.nvars; | |
10 fnodes = fg.nvars+1:N; | |
11 dag = zeros(N); | |
12 for x=1:fg.nvars | |
13 dag(x, fnodes(fg.dep{x})) = 1; | |
14 end | |
15 ns = [fg.node_sizes ones(1, fg.nfactors)]; | |
16 discrete = [fg.dnodes fnodes]; | |
17 bnet = mk_bnet(dag, ns, 'discrete', discrete); | |
18 for x=1:fg.nvars | |
19 bnet.CPD{x} = tabular_CPD(bnet, x, 'CPT', 'unif'); | |
20 end | |
21 ev = cell(1, fg.nvars); % no evidence | |
22 for i=1:fg.nfactors | |
23 f = fnodes(i); | |
24 e = fg.equiv_class(i); | |
25 pot = convert_to_pot(fg.factors{e}, 'd', fg.dom{i}, ev); | |
26 m = pot_to_marginal(pot); | |
27 bnet.CPD{f} = tabular_CPD(bnet, f, 'CPT', m.T); | |
28 end | |
29 | |
30 |