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

first hg version after svn

+% Try to learn a 3 level HHMM similar to mk_square_hhmm
+% from hand-drawn squares.
+
+% Because startprob should be shared for t=1:T,
+% but in the DBN is shared for t=2:T, we train using a single long sequence.
+
+discrete_obs = 0;
+supervised = 1;
+obs_finalF2 = 0;
+% 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.
+
+seed = 1;
+rand('state', seed);
+randn('state', seed);
+
+bnet = 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];
+Qsizes = [2 4 1];
+  
+if supervised
+  bnet.observed = [Q1 Q2 Onode];
+else
+  bnet.observed = [Onode];
+end
+
+if obs_finalF2
+  engine = jtree_dbn_inf_engine(bnet);
+  % can't use ndx version because sometimes F2 is hidden, sometimes observed
+  error('can''t observe F when learning')
+else
+  if supervised
+    engine = jtree_ndx_dbn_inf_engine(bnet);
+  else
+    engine = jtree_hmm_inf_engine(bnet);
+  end
+end
+
+load 'square4_cases' % cases{seq}{i,t} for i=1:ss 
+%plot_square_hhmm(cases{1})
+%long_seq = cat(2, cases{:});
+train_cases = cases(1:2);
+long_seq = cat(2, train_cases{:});
+if ~supervised
+  T = size(long_seq,2);
+  for t=1:T
+    long_seq{Q1,t} = [];
+    long_seq{Q2,t} = [];
+  end
+end
+[bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 2);
+
+eclass = bnet2.equiv_class;
+CPDO=struct(bnet2.CPD{eclass(Onode,1)});
+mu = CPDO.mean;
+Sigma = CPDO.cov;
+CPDO_full = CPDO;
+
+% force diagonal covs after training
+for k=1:size(Sigma,3)
+  Sigma(:,:,k) = diag(diag(Sigma(:,:,k)));
+end
+bnet2.CPD{6} = set_fields(bnet.CPD{6}, 'cov', Sigma);
+
+if 0
+  % visualize each model by concatenating means for each model for nsteps in a row
+  nsteps = 5;
+  ev = cell(ss, nsteps*prod(Qsizes(2:3)));
+  t = 1;
+  for q2=1:Qsizes(2)
+    for q3=1:Qsizes(3)
+      for i=1:nsteps
+	ev{Onode,t} = mu(:,q2,q3);
+	ev{Q2,t} = q2;
+	t = t + 1;
+      end
+    end
+  end
+  plot_square_hhmm(ev)      
+end
+
+% bnet3 is the same as the learned model, except we will use it in testing mode
+if supervised
+  bnet3 = bnet2;
+  bnet3.observed = [Onode];
+  engine3 = hmm_inf_engine(bnet3);
+  %engine3 = jtree_ndx_dbn_inf_engine(bnet3);
+else
+  bnet3 = bnet2;
+  engine3 = engine2;
+end
+
+if 0
+  % segment whole sequence
+  mpe = calc_mpe_dbn(engine3, long_seq);
+  pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []);
+end
+
+% segment each sequence
+test_cases = cases(3:4);
+for i=1:2
+  ev = test_cases{i};
+  T = size(ev, 2);
+  for t=1:T
+    ev{Q1,t} = [];
+    ev{Q2,t} = [];
+  end
+  %mpe = calc_mpe_dbn(engine3, ev);
+  mpe = find_mpe(engine3, ev)
+  subplot(1,2,i)
+  plot_square_hhmm(mpe)      
+  %pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []);
+  q1s = cell2num(mpe(Q1,:));
+  h = hist(q1s, 1:Qsizes(1));
+  map_q1 = argmax(h);
+  str = sprintf('test seq %d is of type %d\n', i, map_q1);
+  title(str)
+end
+
+
+if 0
+% Estimate gotten by couting transitions in the labelled data
+% Note that a self transition shouldnt count if F2=off.
+Q2ev = cell2num(ev(Q2,:));
+Q2a = Q2ev(1:end-1);
+Q2b = Q2ev(2:end);
+counts = compute_counts([Q2a; Q2b], [4 4]);
+end
+
+eclass = bnet2.equiv_class;
+CPDQ1=struct(bnet2.CPD{eclass(Q1,2)});
+CPDQ2=struct(bnet2.CPD{eclass(Q2,2)});
+CPDQ3=struct(bnet2.CPD{eclass(Q3,2)});
+CPDF2=struct(bnet2.CPD{eclass(F2,1)});
+CPDF3=struct(bnet2.CPD{eclass(F3,1)});
+
+
+A=add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2));
+squeeze(A(:,1,:));
+CPDQ2.startprob;
+ 
+if 0
+S=struct(CPDF2.sub_CPD_term);
+S.nsamples
+reshape(S.counts, [2 4 2])
+end