Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/learn_map.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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seed = 1; rand('state', seed); randn('state', seed); obs_model = 'unique'; % each cell has a unique label (essentially fully observable) %obs_model = 'four'; % each cell generates 4 observations, NESW % Generate the true network, and a randomization of it realnet = mk_map_hhmm('p', 0.9, 'obs_model', obs_model); rndnet = mk_rnd_map_hhmm('obs_model', obs_model); eclass = realnet.equiv_class; U = 1; A = 2; C = 3; F = 4; onodes = 5; ss = realnet.nnodes_per_slice; T = 100; evidence = sample_dbn(realnet, 'length', T); ev = cell(ss,T); ev(onodes,:) = evidence(onodes,:); infeng = jtree_dbn_inf_engine(rndnet); if 0 % suppose we do not observe the final finish node, but only know % it is more likely to be on that off ev2 = ev; infeng = enter_evidence(infeng, ev2, 'soft_evidence_nodes', [F T], 'soft_evidence', {[0.3 0.7]'}); end learnednet = learn_params_dbn_em(infeng, {evidence}, 'max_iter', 5); disp('real model') disp_map_hhmm(realnet) disp('learned model') disp_map_hhmm(learnednet) disp('rnd model') disp_map_hhmm(rndnet)