Daniel@0: % Compare the speeds of various inference engines on a coupled HMM
Daniel@0: 
Daniel@0: N = 2;
Daniel@0: Q = 2;
Daniel@0: rand('state', 0);
Daniel@0: randn('state', 0);
Daniel@0: discrete = 1;
Daniel@0: if discrete
Daniel@0:   Y = 2; % size of output alphabet
Daniel@0: else
Daniel@0:   Y = 1;
Daniel@0: end
Daniel@0: coupled = 1;
Daniel@0: [bnet, onodes] = mk_chmm(N, Q, Y, discrete, coupled);
Daniel@0: ss = N*2;
Daniel@0: 
Daniel@0: T = 3;
Daniel@0: 
Daniel@0: 
Daniel@0: engine = {};
Daniel@0: tic; engine{end+1} = jtree_dbn_inf_engine(bnet, 'observed', onodes);  toc
Daniel@0: %tic; engine{end+1} = jtree_ndxSD_dbn_inf_engine(bnet, onodes);  toc
Daniel@0: %tic; engine{end+1} = jtree_ndxB_dbn_inf_engine(bnet, onodes);  toc
Daniel@0: engine{end+1} = hmm_inf_engine(bnet, onodes);
Daniel@0: %engine{end+1} = dhmm_inf_engine(bnet, onodes);
Daniel@0: tic; engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T, onodes);  toc
Daniel@0: 
Daniel@0: %engine{end+1} = bk_inf_engine(bnet, 'ff', onodes);
Daniel@0: %engine{end+1} = loopy_dbn_inf_engine(bnet, onodes);
Daniel@0: 
Daniel@0: exact = [1 2 3];
Daniel@0: 
Daniel@0: filter = 0;
Daniel@0: single = 0;
Daniel@0: maximize = 0;
Daniel@0: 
Daniel@0: [err, time, engine] = cmp_inference(bnet, onodes, engine, exact, T, filter, single, maximize);
Daniel@0: %err = cmp_learning(bnet, onodes, engine, exact, T);
Daniel@0: 
Daniel@0: