|
wolffd@0
|
1 function CPD = bayes_update_params(CPD, self_ev, pev)
|
|
wolffd@0
|
2 % UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (tabular)
|
|
wolffd@0
|
3 % CPD = update_params_complete(CPD, self_ev, pev)
|
|
wolffd@0
|
4 %
|
|
wolffd@0
|
5 % self_ev(m) is the evidence on this node in case m.
|
|
wolffd@0
|
6 % pev(i,m) is the evidence on the i'th parent in case m (if there are any parents).
|
|
wolffd@0
|
7 % These can be arrays or cell arrays.
|
|
wolffd@0
|
8 %
|
|
wolffd@0
|
9 % We update the Dirichlet pseudo counts and set the CPT to the mean of the posterior.
|
|
wolffd@0
|
10
|
|
wolffd@0
|
11 if iscell(self_ev), usecell = 1; else usecell = 0; end
|
|
wolffd@0
|
12
|
|
wolffd@0
|
13 ncases = length(self_ev);
|
|
wolffd@0
|
14 sz = CPD.sizes;
|
|
wolffd@0
|
15 nparents = length(sz)-1;
|
|
wolffd@0
|
16 assert(nparents == size(pev,1));
|
|
wolffd@0
|
17
|
|
wolffd@0
|
18 if ncases == 0 | ~adjustable_CPD(CPD)
|
|
wolffd@0
|
19 return;
|
|
wolffd@0
|
20 elseif ncases == 1 % speedup the sequential learning case by avoiding normalization of the whole array
|
|
wolffd@0
|
21 if usecell
|
|
wolffd@0
|
22 x = cat(1, pev{:})';
|
|
wolffd@0
|
23 y = self_ev{1};
|
|
wolffd@0
|
24 else
|
|
wolffd@0
|
25 x = pev(:)';
|
|
wolffd@0
|
26 y = self_ev;
|
|
wolffd@0
|
27 end
|
|
wolffd@0
|
28 switch nparents
|
|
wolffd@0
|
29 case 0,
|
|
wolffd@0
|
30 CPD.dirichlet(y) = CPD.dirichlet(y)+1;
|
|
wolffd@0
|
31 CPD.CPT = CPD.dirichlet / sum(CPD.dirichlet);
|
|
wolffd@0
|
32 case 1,
|
|
wolffd@0
|
33 CPD.dirichlet(x(1), y) = CPD.dirichlet(x(1), y)+1;
|
|
wolffd@0
|
34 CPD.CPT(x(1), :) = CPD.dirichlet(x(1), :) ./ sum(CPD.dirichlet(x(1), :));
|
|
wolffd@0
|
35 case 2,
|
|
wolffd@0
|
36 CPD.dirichlet(x(1), x(2), y) = CPD.dirichlet(x(1), x(2), y)+1;
|
|
wolffd@0
|
37 CPD.CPT(x(1), x(2), :) = CPD.dirichlet(x(1), x(2), :) ./ sum(CPD.dirichlet(x(1), x(2), :));
|
|
wolffd@0
|
38 case 3,
|
|
wolffd@0
|
39 CPD.dirichlet(x(1), x(2), x(3), y) = CPD.dirichlet(x(1), x(2), x(3), y)+1;
|
|
wolffd@0
|
40 CPD.CPT(x(1), x(2), x(3), :) = CPD.dirichlet(x(1), x(2), x(3), :) ./ sum(CPD.dirichlet(x(1), x(2), x(3), :));
|
|
wolffd@0
|
41 otherwise,
|
|
wolffd@0
|
42 ind = subv2ind(sz, [x y]);
|
|
wolffd@0
|
43 CPD.dirichlet(ind) = CPD.dirichlet(ind) + 1;
|
|
wolffd@0
|
44 CPD.CPT = mk_stochastic(CPD.dirichlet);
|
|
wolffd@0
|
45 end
|
|
wolffd@0
|
46 else
|
|
wolffd@0
|
47 if usecell
|
|
wolffd@0
|
48 data = [cell2num(pev); cell2num(self_ev)];
|
|
wolffd@0
|
49 else
|
|
wolffd@0
|
50 data = [pev; self_ev];
|
|
wolffd@0
|
51 end
|
|
wolffd@0
|
52 counts = compute_counts(data, sz);
|
|
wolffd@0
|
53 CPD.dirichlet = CPD.dirichlet + counts;
|
|
wolffd@0
|
54 CPD.CPT = mk_stochastic(CPD.dirichlet);
|
|
wolffd@0
|
55 end
|
