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