diff toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,171 @@
+% 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!