Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_hmm_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 = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) | |
2 % MK_HMM_BNET Make a (static) bnet to represent a hidden Markov model | |
3 % bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) | |
4 % | |
5 % T = num time slices | |
6 % Q = num hidden states | |
7 % O = size of the observed node (num discrete values or length of vector) | |
8 % cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete | |
9 % param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node | |
10 | |
11 N = 2*T; | |
12 dag = zeros(N); | |
13 %hnodes = 1:2:2*T; | |
14 hnodes = 1:T; | |
15 for i=1:T-1 | |
16 dag(hnodes(i), hnodes(i+1))=1; | |
17 end | |
18 %onodes = 2:2:2*T; | |
19 onodes = T+1:2*T; | |
20 for i=1:T | |
21 dag(hnodes(i), onodes(i)) = 1; | |
22 end | |
23 | |
24 if cts_obs | |
25 dnodes = hnodes; | |
26 else | |
27 dnodes = 1:N; | |
28 end | |
29 ns = ones(1,N); | |
30 ns(hnodes) = Q; | |
31 ns(onodes) = O; | |
32 | |
33 if param_tying | |
34 H1class = 1; Hclass = 2; Oclass = 3; | |
35 eclass = ones(1,N); | |
36 eclass(hnodes(2:end)) = Hclass; | |
37 eclass(hnodes(1)) = H1class; | |
38 eclass(onodes) = Oclass; | |
39 else | |
40 eclass = 1:N; | |
41 end | |
42 | |
43 bnet = mk_bnet(dag, ns, 'observed', onodes, 'discrete', dnodes, 'equiv_class', eclass); | |
44 | |
45 hnodes = mysetdiff(1:N, onodes); | |
46 if ~param_tying | |
47 for i=hnodes(:)' | |
48 bnet.CPD{i} = tabular_CPD(bnet, i); | |
49 end | |
50 if cts_obs | |
51 for i=onodes(:)' | |
52 bnet.CPD{i} = gaussian_CPD(bnet, i); | |
53 end | |
54 else | |
55 for i=onodes(:)' | |
56 bnet.CPD{i} = tabular_CPD(bnet, i); | |
57 end | |
58 end | |
59 else | |
60 bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior | |
61 bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix | |
62 if cts_obs | |
63 bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1)); | |
64 else | |
65 bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1)); | |
66 end | |
67 end |