function engine = dhmm_inf_engine(bnet, onodes)% DHMM_INF_ENGINE Inference engine for discrete DBNs which uses the forwards-backwards algorithm.% engine = dhmm_inf_engine(bnet, onodes)%% 'onodes' specifies which nodes are observed; these must be leaves, and can be discrete or continuous.% The remaining nodes are all hidden, and must be discrete.% The DBN is converted to an HMM, with a single meganode, but which may have factored obs.ss = length(bnet.intra);hnodes = mysetdiff(1:ss, onodes);evidence = cell(ss, 2);ns = bnet.node_sizes;Q = prod(ns(hnodes));tmp = dpot_to_table(compute_joint_pot(bnet, hnodes, evidence));engine.startprob = reshape(tmp, Q, 1);tmp = dpot_to_table(compute_joint_pot(bnet, [hnodes hnodes+ss], evidence));engine.transprob = mk_stochastic(reshape(tmp, Q, Q));engine.obsprob = cell(1, length(onodes));for i=1:length(onodes)  tmp = dpot_to_table(compute_joint_pot(bnet, [hnodes onodes(i)], evidence));  O = ns(onodes(i));  engine.obsprob{i} = mk_stochastic(reshape(tmp, Q, O));end% This is where we will store the results between enter_evidence and marginal_nodesengine.gamma = [];engine.xi = [];engine.onodes = onodes;engine.hnodes = hnodes;engine.maximize = [];engine = class(engine, 'dhmm_inf_engine', inf_engine(bnet));