function engine = hmm_2TBN_inf_engine(bnet, varargin)% HMM_2TBN_INF_ENGINE Inference engine for DBNs which uses the forwards-backwards algorithm.% engine = hmm_2TBN_inf_engine(bnet, ...)%% The DBN is converted to an HMM with a single meganode, but the observed nodes remain factored.% This can be faster than jtree if the num. hidden nodes is low, because of lower constant factors.%% All hidden nodes must be discrete.% All observed nodes are assumed to be leaves.% The parents of each observed leaf are assumed to be a subset of the hidden nodes within the same slice.% The only exception is if bnet is an AR-HMM, where the parents are assumed to be self in the% previous slice (continuous), plus all the discrete nodes in the current slice.%% Optional arguments%% ndx_type - 'B', 'D', or 'SD', used in marginal_family [ 'SD' ]ndx_type = 'SD';ss = bnet.nnodes_per_slice;% parse optional paramsargs = varargin;nargs = length(args);if nargs > 0  for i=1:2:nargs    switch args{i},     %case 'ndx_type', ndx_type = args{i+1};     otherwise,        error(['invalid argument name ' args{i}]);           end  endend% Stuff to do with speeding up marginal_family%engine.ndx_type = ndx_type;[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter);engine.persist_bitv = zeros(1, ss);engine.persist_bitv(engine.persist) = 1;ns = bnet.node_sizes(:);ns(bnet.observed) = 1;ns(bnet.observed+ss) = 1;engine.eff_node_sizes = ns;% for n=1:ss%   dom = 1:(2*ss); % domain of xi(:,:,1)%   fam = family(bnet.dag, n+ss);%   engine.marg_fam2_ndx_id(n) = add_ndx(dom, fam, ns, ndx_type); %   dom = 1:ss; % domain of gamma(:,:,1)%   fam = family(bnet.dag, n);%   engine.marg_fam1_ndx_id(n) = add_ndx(dom, fam, ns, ndx_type);%   engine.marg_singleton_ndx_id(n) = add_ndx(dom, n, ns, ndx_type);% endfor o=bnet.observed(:)'  %if bnet.equiv_class(o,1) ~= bnet.equiv_class(o,2)  %  error(['observed node ' num2str(o) ' is not tied'])  %end  cs = children(bnet.dag, o);  if ~isempty(cs)    error(['observed node ' num2str(o) ' is not allowed children'])  endend[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet);% This is where we will store the results between enter_evidence and marginal_nodesengine.one_slice_marginal = [];engine.two_slice_marginal = [];ss = length(bnet.intra);engine.maximize = [];engine.evidence = [];engine.node_sizes = [];% avoid the need to do bnet_from_engine, which is slowengine.slice_size = ss;engine.parents = bnet.parents;engine.bel = [];engine = class(engine, 'hmm_2TBN_inf_engine', inf_engine(bnet));