% Compare the speeds of various inference engines on a coupled HMMN = 3;Q = 2;rand('state', 0);randn('state', 0);discrete = 0;if discrete  Y = 2; % size of output alphabetelse  Y = 3; % size of observed vectorsendcoupled = 1;bnet = mk_chmm(N, Q, Y, discrete, coupled); %bnet = mk_fhmm(N, Q, Y, discrete);  % factorial HMMss = length(bnet.node_sizes_slice);T = 3;USEC = exist('@jtree_C_inf_engine/collect_evidence','file');engine = {};engine{end+1} = jtree_dbn_inf_engine(bnet);%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'SD');%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'D');%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'B');if USEC, engine{end+1} = jtree_C_dbn_inf_engine(bnet); endengine{end+1} = hmm_inf_engine(bnet);engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); % times in matlab N=4 Q=4 T=5 (* = winner)%     jtree    SD       B          hmm       dhmm      unrolled%    0.6266    1.1563    8.3815    0.3069    0.1948*    0.8654  inf%    0.9057*   2.1522   12.6314    2.6847    2.3107    3.1905  learn%engine{end+1} = bk_inf_engine(bnet, 'ff', onodes);%engine{end+1} = pearl_unrolled_dbn_inf_engine(bnet, T);inf_time = cmp_inference_dbn(bnet, engine, T)learning_time = cmp_learning_dbn(bnet, engine, T)