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view toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/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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function [engine, loglik] = enter_evidence(engine, evidence, filter) % ENTER_EVIDENCE Add the specified evidence to the network (pearl_dbn) % [engine, loglik] = enter_evidence(engine, evidence, filter) % % evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) % If filter = 1, we do filtering, otherwise smoothing (default). if nargin < 3, filter = 0; end [ss T] = size(evidence); bnet = bnet_from_engine(engine); bnet2 = dbn_to_bnet(bnet, T); ns = bnet2.node_sizes; hnodes = mysetdiff(1:ss, engine.onodes); hnodes = hnodes(:)'; [engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); msg = init_msgs(bnet2.dag, ns, evidence); msg = init_ev_msgs(engine, evidence, msg); niter = 1; for iter=1:niter % FORWARD for t=1:T % update pi for i=1:ss %hnodes n = i + (t-1)*ss; ps = parents(bnet2.dag, n); if t==1 e = bnet.equiv_class(i,1); else e = bnet.equiv_class(i,2); end msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); %msg{n}.pi = normalise(msg{n}.pi(:) .* msg{n}.lambda_from_self(:)); end % send pi msg to children for i=1:ss % hnodes n = i + (t-1)*ss; cs = children(bnet2.dag, n); for c=cs(:)' j = engine.parent_index{c}(n); % n is c's j'th parent msg{c}.pi_from_parent{j} = normalise(compute_pi_msg(n, cs, msg, c, ns)); end end end if filter disp('skipping smoothing'); break; end % BACKWARD for t=T:-1:1 % update lambda for i=1:ss % hnodes n = i + (t-1)*ss; cs = children(bnet2.dag, n); msg{n}.lambda = compute_lambda(n, cs, msg, ns); end % send lambda msgs to parents for i=1:ss % hnodes n = i + (t-1)*ss; ps = parents(bnet2.dag, n); for p=ps(:)' j = engine.child_index{p}(n); % n is p's j'th child if t > 1 e = bnet.equiv_class(i, 2); else e = bnet.equiv_class(i, 1); end msg{p}.lambda_from_child{j} = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); end end end end engine.marginal = cell(ss,T); lik = zeros(1,ss*T); for t=1:T for i=1:ss n = i + (t-1)*ss; [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); engine.marginal{i,t} = bel; end end engine.evidence = evidence; % needed by marginal_nodes and marginal_family engine.msg = msg; % needed by marginal_family loglik = sum(log(lik)); %%%%%%% function lambda = compute_lambda(n, cs, msg, ns) % Pearl p183 eq 4.50 lambda = prod_lambda_msgs(n, cs, msg, ns); %%%%%%% function pi_msg = compute_pi_msg(n, cs, msg, c, ns) % Pearl p183 eq 4.53 and 4.51 pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); %%%%%%%%% function lam = prod_lambda_msgs(n, cs, msg, ns, except) if nargin < 5, except = -1; end lam = msg{n}.lambda_from_self(:); %lam = ones(ns(n), 1); for i=1:length(cs) c = cs(i); if c ~= except lam = lam .* msg{n}.lambda_from_child{i}; end end