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% Compare the speeds of various inference engines on a coupled HMM
N = 3;
Q = 2;
rand('state', 0);
randn('state', 0);
discrete = 0;
if discrete
Y = 2; % size of output alphabet
else
Y = 3; % size of observed vectors
end
coupled = 1;
bnet = mk_chmm(N, Q, Y, discrete, coupled);
%bnet = mk_fhmm(N, Q, Y, discrete); % factorial HMM
ss = 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); end
engine{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)
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