Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/enter_evidence.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 [engine, loglik] = enter_evidence(engine, evidence, varargin) | |
2 % ENTER_EVIDENCE Add the specified evidence to the network (jtree) | |
3 % [engine, loglik] = enter_evidence(engine, evidence, ...) | |
4 % | |
5 % evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). | |
6 % | |
7 % The following optional arguments can be specified in the form of name/value pairs: | |
8 % [default value in brackets] | |
9 % | |
10 % soft - a cell array of soft/virtual evidence; | |
11 % soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] | |
12 % | |
13 % e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) | |
14 | |
15 bnet = bnet_from_engine(engine); | |
16 ns = bnet.node_sizes(:); | |
17 N = length(bnet.dag); | |
18 | |
19 engine.evidence = evidence; % store this for marginal_nodes with add_ev option | |
20 engine.maximize = 0; | |
21 | |
22 % set default params | |
23 exclude = []; | |
24 soft_evidence = cell(1,N); | |
25 | |
26 % parse optional params | |
27 args = varargin; | |
28 nargs = length(args); | |
29 for i=1:2:nargs | |
30 switch args{i}, | |
31 case 'soft', soft_evidence = args{i+1}; | |
32 case 'maximize', engine.maximize = args{i+1}; | |
33 otherwise, | |
34 error(['invalid argument name ' args{i}]); | |
35 end | |
36 end | |
37 | |
38 onodes = find(~isemptycell(evidence)); | |
39 hnodes = find(isemptycell(evidence)); | |
40 pot_type = determine_pot_type(bnet, onodes); | |
41 if strcmp(pot_type, 'cg') | |
42 check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); | |
43 end | |
44 | |
45 if is_mnet(bnet) | |
46 pot = engine.user_pot; | |
47 clqs = engine.nums_ass_to_user_clqs; | |
48 else | |
49 % Evaluate CPDs with evidence, and convert to potentials | |
50 pot = cell(1, N); | |
51 for n=1:N | |
52 fam = family(bnet.dag, n); | |
53 e = bnet.equiv_class(n); | |
54 if isempty(bnet.CPD{e}) | |
55 error(['must define CPD ' num2str(e)]) | |
56 else | |
57 pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); | |
58 end | |
59 end | |
60 clqs = engine.clq_ass_to_node(1:N); | |
61 end | |
62 | |
63 % soft evidence | |
64 soft_nodes = find(~isemptycell(soft_evidence)); | |
65 S = length(soft_nodes); | |
66 if S > 0 | |
67 assert(pot_type == 'd'); | |
68 assert(mysubset(soft_nodes, bnet.dnodes)); | |
69 end | |
70 for i=1:S | |
71 n = soft_nodes(i); | |
72 pot{end+1} = dpot(n, ns(n), soft_evidence{n}); | |
73 end | |
74 clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; | |
75 | |
76 | |
77 [clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); | |
78 [clpot, seppot] = collect_evidence(engine, clpot, seppot); | |
79 [clpot, seppot] = distribute_evidence(engine, clpot, seppot); | |
80 | |
81 C = length(clpot); | |
82 ll = zeros(1, C); | |
83 for i=1:C | |
84 [clpot{i}, ll(i)] = normalize_pot(clpot{i}); | |
85 end | |
86 loglik = ll(1); % we can extract the likelihood from any clique | |
87 | |
88 engine.clpot = clpot; |