Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_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, varargin) % ENTER_EVIDENCE Add the specified evidence to the network (jtree) % [engine, loglik] = enter_evidence(engine, evidence, ...) % % evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). % % The following optional arguments can be specified in the form of name/value pairs: % [default value in brackets] % % maximize - if 1, does max-product instead of sum-product [0] % soft - a cell array of soft/virtual evidence; % soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] % % e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) % % For backwards compatibility with BNT2, you can also specify the parameters in the following order % engine = enter_evidence(engine, ev, soft_ev) bnet = bnet_from_engine(engine); ns = bnet.node_sizes(:); N = length(bnet.dag); % set default params exclude = []; soft_evidence = cell(1,N); maximize = 0; % parse optional params args = varargin; nargs = length(args); if nargs > 0 if iscell(args{1}) soft_evidence = args{1}; else for i=1:2:nargs switch args{i}, case 'soft', soft_evidence = args{i+1}; case 'maximize', maximize = args{i+1}; otherwise, error(['invalid argument name ' args{i}]); end end end end engine.maximize = maximize; onodes = find(~isemptycell(evidence)); hnodes = find(isemptycell(evidence)); pot_type = determine_pot_type(bnet, onodes); if strcmp(pot_type, 'cg') check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); end hard_nodes = 1:N; soft_nodes = find(~isemptycell(soft_evidence)); S = length(soft_nodes); if S > 0 assert(pot_type == 'd'); assert(mysubset(soft_nodes, bnet.dnodes)); end % Evaluate CPDs with evidence, and convert to potentials pot = cell(1, N+S); for n=1:N fam = family(bnet.dag, n); e = bnet.equiv_class(n); pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); end for i=1:S n = soft_nodes(i); pot{N+i} = dpot(n, ns(n), soft_evidence{n}); end %clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); %[clpot, loglik] = enter_soft_evidence(engine, clqs, pot, onodes, pot_type); %engine.clpot = clpot; % save the results for marginal_nodes clique = engine.clq_ass_to_node([hard_nodes soft_nodes]); potential = pot; % Set the clique potentials to all 1s C = length(engine.cliques); for i=1:C engine.clpot{i} = mk_initial_pot(pot_type, engine.cliques{i}, ns, bnet.cnodes, onodes); end % Multiply on specified potentials for i=1:length(clique) c = clique(i); engine.clpot{c} = multiply_by_pot(engine.clpot{c}, potential{i}); end root = 1; % arbitrary engine = collect_evidence(engine, root); engine = distribute_evidence(engine, root); ll = zeros(1, C); for i=1:C [engine.clpot{i}, ll(i)] = normalize_pot(engine.clpot{i}); end loglik = ll(1); % we can extract the likelihood from any clique