1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
|
function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter)
% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (bk)
% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter)
[ss T] = size(CPDpot);
C = length(engine.clusters);
Q = length(cliques_from_engine(engine.sub_engine));
Q1 = length(cliques_from_engine(engine.sub_engine1));
clpot = cell(Q,T);
alpha = cell(C,T);
% Forwards
% The method is a generalization of the following HMM equation:
% alpha(j,t) = normalise( (sum_i alpha(i,t-1) * transmat(i,j)) * obsmat(j,t) )
% where alpha(j,t) = Pr(Q(t)=j | y(1:t))
t = 1;
[clpot(1:Q1,t), logscale(t)] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1(:), ...
CPDpot(:,1), find(observed(:,1)), pot_type);
for c=1:C
k = engine.clq_ass_to_cluster1(c);
alpha{c,t} = marginalize_pot(clpot{k,t}, engine.clusters{c});
end
% For filtering, clpot{1} contains evidence on slice 1 only
%fprintf('alphas t=%d\n', t);
%for c=1:8
% temp = pot_to_marginal(alpha{c,t});
% temp.T
%end
% clpot{t} contains evidence from slices t-1, t for t > 1
clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)];
for t=2:T
pots = [alpha(:,t-1); CPDpot(:,t)];
[clpot(:,t), logscale(t)] = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t-1:t)), pot_type);
for c=1:C
k = engine.clq_ass_to_cluster(c,2);
cl = engine.clusters{c};
alpha{c,t} = marginalize_pot(clpot{k,t}, cl+ss); % extract slice 2 posterior
alpha{c,t} = set_domain_pot(alpha{c,t}, cl); % shift back to slice 1 for re-use as prior
end
end
loglik = sum(logscale);
if filter
return;
end
% Backwards
% The method is a generalization of the following HMM equation:
% beta(i,t) = (sum_j transmat(i,j) * obsmat(j,t+1) * beta(j,t+1))
% where beta(i,t) = Pr(y(t+1:T) | Q(t)=i)
t = T;
bnet = bnet_from_engine(engine);
beta = cell(C,T);
for c=1:C
beta{c,t} = mk_initial_pot(pot_type, engine.clusters{c} + ss, bnet.node_sizes(:), bnet.cnodes(:), ...
find(observed(:,t-1:t)));
end
for t=T-1:-1:1
clqs = [engine.clq_ass_to_cluster(:,2); engine.clq_ass_to_node(:,2)];
pots = [beta(:,t+1); CPDpot(:,t+1)];
temp = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type);
for c=1:C
k = engine.clq_ass_to_cluster(c,1);
cl = engine.clusters{c};
beta{c,t} = marginalize_pot(temp{k}, cl); % extract slice 1
beta{c,t} = set_domain_pot(beta{c,t}, cl + ss); % shift fwd to slice 2
end
end
% Combine
% The method is a generalization of the following HMM equation:
% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) )
% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T))
for t=1:T-1
clqs = [engine.clq_ass_to_cluster(:); engine.clq_ass_to_node(:,2)];
pots = [alpha(:,t); beta(:,t+1); CPDpot(:,t+1)];
clpot(:,t+1) = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type);
end
% for smoothing, clpot{1} is undefined
for k=1:Q1
clpot{k,1} = [];
end
|
