Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.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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% Try to learn a 3 level HHMM similar to mk_square_hhmm % from synthetic discrete sequences discrete_obs = 1; supervised = 0; obs_finalF2 = 0; seed = 1; rand('state', seed); randn('state', seed); bnet_init = mk_square_hhmm(discrete_obs, 0); ss = 6; Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; if supervised bnet_init.observed = [Q1 Q2 Onode]; else bnet_init.observed = [Onode]; end if obs_finalF2 engine_init = jtree_dbn_inf_engine(bnet_init); % can't use ndx version because sometimes F2 is hidden, sometimes observed error('can''t observe F when learning') % It is not possible to observe F2 if we learn % because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume % the F nodes are always hidden (for speed). % However, for generating, we might want to set the final F2=true % to force all subroutines to finish. else if supervised engine_init = jtree_ndx_dbn_inf_engine(bnet_init); else engine_init = hmm_inf_engine(bnet_init); end end % generate some synthetic data (easier to debug) chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; L=find(chars=='L'); l=find(chars=='l'); U=find(chars=='U'); u=find(chars=='u'); R=find(chars=='R'); r=find(chars=='r'); D=find(chars=='D'); d=find(chars=='d'); cases = {}; T = 8; ev = cell(ss, T); ev(Onode,:) = num2cell([L l U u R r D d]); if supervised ev(Q1,:) = num2cell(1*ones(1,T)); ev(Q2,:) = num2cell( [1 1 2 2 3 3 4 4]); end cases{1} = ev; cases{3} = ev; T = 8; ev = cell(ss, T); %we start with R then r, even though we are running the model 'backwards'! ev(Onode,:) = num2cell([R r U u L l D d]); if supervised ev(Q1,:) = num2cell(2*ones(1,T)); ev(Q2,:) = num2cell( [3 3 2 2 1 1 4 4]); end cases{2} = ev; cases{4} = ev; if obs_finalF2 for i=1:length(cases) T = size(cases{i},2); cases{i}(F2,T)={2}; % force F2 to be finished at end of seq end end % startprob should be shared for t=1:T, % but in the DBN it is shared for t=2:T, % so we train using a single long sequence. long_seq = cat(2, cases{:}); [bnet_learned, LL, engine_learned] = ... learn_params_dbn_em(engine_init, {long_seq}, 'max_iter', 200); % figure out which subsequence each model is responsible for mpe = calc_mpe_dbn(engine_learned, long_seq); pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, chars); % The "true" segmentation of the training sequence is % Q1: 1 2 % O: L l U u R r D d | R r U u L l D d | etc. % % When we learn in a supervised fashion, we recover the "truth". % When we learn in an unsupervised fashion with seed=1, we get % Q1: 2 1 % O: L l U u R r D d R r | U u L l D d | etc. % % This means for model 1: % starts in state 2 % transitions 2->1, 1->4, 4->e, 3->2 % % For model 2, % starts in state 1 % transitions 1->2, 2->3, 3->4 or e, 4->3 % examine the params eclass = bnet_learned.equiv_class; CPDQ1=struct(bnet_learned.CPD{eclass(Q1,2)}); CPDQ2=struct(bnet_learned.CPD{eclass(Q2,2)}); CPDQ3=struct(bnet_learned.CPD{eclass(Q3,2)}); CPDF2=struct(bnet_learned.CPD{eclass(F2,1)}); CPDF3=struct(bnet_learned.CPD{eclass(F3,1)}); CPDO=struct(bnet_learned.CPD{eclass(Onode,1)}); A_learned =add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2)); squeeze(A_learned(:,1,:)) squeeze(A_learned(:,2,:)) % Does the "true" model have higher likelihood than the learned one? % i.e., Does the unsupervised method learn the wrong model because % we have the wrong cost fn, or because of local minima? bnet_true = mk_square_hhmm(discrete_obs,1); % examine the params eclass = bnet_learned.equiv_class; CPDQ1_true=struct(bnet_true.CPD{eclass(Q1,2)}); CPDQ2_true=struct(bnet_true.CPD{eclass(Q2,2)}); CPDQ3_true=struct(bnet_true.CPD{eclass(Q3,2)}); CPDF2_true=struct(bnet_true.CPD{eclass(F2,1)}); CPDF3_true=struct(bnet_true.CPD{eclass(F3,1)}); A_true =add_hhmm_end_state(CPDQ2_true.transprob, CPDF2_true.termprob(:,:,2)); squeeze(A_true(:,1,:)) if supervised engine_true = jtree_ndx_dbn_inf_engine(bnet_true); else engine_true = hmm_inf_engine(bnet_true); end %[engine_learned, ll_learned] = enter_evidence(engine_learned, long_seq); %[engine_true, ll_true] = enter_evidence(engine_true, long_seq); [engine_learned, ll_learned] = enter_evidence(engine_learned, cases{2}); [engine_true, ll_true] = enter_evidence(engine_true, cases{2}); ll_learned ll_true % remove concatentation artefacts ll_learned = 0; ll_true = 0; for m=1:length(cases) [engine_learned, ll_learned_tmp] = enter_evidence(engine_learned, cases{m}); [engine_true, ll_true_tmp] = enter_evidence(engine_true, cases{m}); ll_learned = ll_learned + ll_learned_tmp; ll_true = ll_true + ll_true_tmp; end ll_learned ll_true % In both cases, ll_learned >> ll_true % which shows we are using the wrong cost function!