Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/bk_inf_engine.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 = bk_inf_engine(bnet, varargin) % BK_INF_ENGINE Boyen-Koller approximate inference algorithm for DBNs. % % In the BK algorithm, the belief state is represented as a product of marginals, % even though the factors may not be independent. % % engine = bk_inf_engine(bnet, ...) % % The following optional arguments can be specified in the form of name/value pairs: % [default value in brackets] % % clusters - if a cell array, clusters{i} specifies the terms in the i'th factor. % - 'exact' means create one cluster that contains all the nodes in a slice [exact] % - 'ff' means create one cluster per node (ff = fully factorised). % % % For details, see % - "Tractable Inference for Complex Stochastic Processes", X. Boyen and D. Koller, UAI 98. % - "Approximate learning of dynamic models", X. Boyen and D. Koller, NIPS 98. % (The UAI98 paper discusses filtering and theory, and the NIPS98 paper discusses smoothing.) ss = length(bnet.intra); % set default params clusters = 'exact'; if nargin >= 2 args = varargin; nargs = length(args); for i=1:2:nargs switch args{i}, case 'clusters', clusters = args{i+1}; otherwise, error(['unrecognized argument ' args{i}]) end end end if strcmp(clusters, 'exact') %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) }; clusters = { 1:ss }; elseif strcmp(clusters, 'ff') clusters = num2cell(1:ss); end % We need to insert the prior on the clusters in slice 1, % and extract the posterior on the clusters in slice 2. C = length(clusters); clusters2 = cell(1,2*C); clusters2(1:C) = clusters; for c=1:C clusters2{c+C} = clusters{c} + ss; end onodes = bnet.observed; obs_nodes = [onodes(:) onodes(:)+ss]; engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2); engine.clq_ass_to_cluster = zeros(C, 2); for c=1:C engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c}); engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss); end engine.clusters = clusters; engine.clq_ass_to_node = zeros(ss, 2); for i=1:ss engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i); engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss); end % Also create an engine just for slice 1 bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ... 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); for i=1:max(bnet1.equiv_class) bnet1.CPD{i} = bnet.CPD{i}; end engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters); engine.clq_ass_to_cluster1 = zeros(1,C); for c=1:C engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c}); end engine.clq_ass_to_node1 = zeros(1, ss); for i=1:ss engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i); end engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes engine.filter = []; engine.maximize = []; engine.T = []; engine.bel = []; engine.bel_clpot = []; engine.slice1 = []; %engine.pot_type = 'cg'; % hack for online inference so we can cope with hidden Gaussians and discrete % it will not affect the pot type used in enter_evidence engine.pot_type = determine_pot_type(bnet, onodes); engine = class(engine, 'bk_inf_engine', inf_engine(bnet));