BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

7 files changed, 676 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Entries
new file mode 100644
index 00000000..6d415d0d
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Entries
@@ -0,0 +1,5 @@
+/learn_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/mk_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/plot_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/sample_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Repository
new file mode 100644
index 00000000..47df1a8c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/dynamic/HHMM/Square/Old
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Root b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m
new file mode 100644
index 00000000..695ae047
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m
@@ -0,0 +1,294 @@
+% Learn a 3 level HHMM similar to mk_square_hhmm
+
+% 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.
+
+ss = 6;
+Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6;
+Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3];
+
+seed = 1;
+rand('state', seed);
+randn('state', seed);
+
+if discrete_obs
+  Qsizes = [2 4 2];
+else
+  Qsizes = [2 4 1];
+end
+
+D = 3;
+Qnodes = 1:D;
+startprob = cell(1,D);
+transprob = cell(1,D);
+termprob = cell(1,D);
+
+startprob{1} = 'unif';
+transprob{1} = 'unif';
+
+% In the unsupervised case, it is essential that we break symmetry
+% in the initial param estimates.
+%startprob{2} = 'unif';
+%transprob{2} = 'unif';
+%termprob{2} = 'unif';
+startprob{2} = 'rnd';
+transprob{2} = 'rnd';
+termprob{2} = 'rnd';
+
+leftright = 0;
+if leftright
+  % Initialise base-level models as left-right.
+  % If we initialise with delta functions,
+  % they will remain delat funcitons after learning
+  startprob{3} = 'leftstart';
+  transprob{3}  = 'leftright';
+  termprob{3} = 'rightstop';
+else
+  % If we want to be able to run a base-level model backwards...
+  startprob{3} = 'rnd';
+  transprob{3}  = 'rnd';
+  termprob{3} = 'rnd';
+end
+
+if discrete_obs
+  % Initialise observations of lowest level primitives in a way which we can interpret
+  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');
+  Osize = length(chars);
+  
+  p = 0.9;
+  obsprob = (1-p)*ones([4 2 Osize]);
+  %       Q2 Q3 O
+  obsprob(1, 1, L) =  p;
+  obsprob(1, 2, l) =  p;
+  obsprob(2, 1, U) =  p;
+  obsprob(2, 2, u) =  p;
+  obsprob(3, 1, R) =  p;
+  obsprob(3, 2, r) =  p;
+  obsprob(4, 1, D) =  p;
+  obsprob(4, 2, d) =  p;
+  obsprob = mk_stochastic(obsprob);
+  Oargs = {'CPT', obsprob};
+
+else
+  % Initialise means of lowest level primitives in a way which we can interpret
+  % These means are little vectors in the east, south, west, north directions.
+  % (left-right=east, up-down=south, right-left=west, down-up=north)
+  Osize = 2;
+  mu = zeros(2, Qsizes(2), Qsizes(3));
+  noise = 0;
+  scale = 3;
+  for q3=1:Qsizes(3)
+    mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1);
+  end
+  for q3=1:Qsizes(3)
+    mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1);
+  end
+  for q3=1:Qsizes(3)
+    mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1);
+  end
+  for q3=1:Qsizes(3)
+    mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1);
+  end
+  Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]);
+  Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'};
+end
+
+bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs,...
+	       'Oargs', Oargs, 'Ops', Qnodes(2:3), ...
+	       'startprob', startprob, 'transprob', transprob, 'termprob', termprob);
+
+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
+  
+if discrete_obs
+  % generate some synthetic data (easier to debug)
+  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);
+  if leftright % base model is left-right
+    ev(Onode,:) = num2cell([R r U u L l D d]);
+  else
+    ev(Onode,:) = num2cell([r R u U l L d D]);
+  end
+  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
+
+  if 0
+    ev = cases{4};
+    engine2 = enter_evidence(engine2, ev);
+    T = size(ev,2);
+    for t=1:T
+      m=marginal_family(engine2, F2, t);
+      fprintf('t=%d\n', t);
+      reshape(m.T, [2 2])
+    end
+  end
+  
+  %  [bnet2, LL] = learn_params_dbn_em(engine, cases, 'max_iter', 10);
+  long_seq = cat(2, cases{:});
+  [bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 200);
+  
+  % figure out which subsequence each model is responsible for
+  mpe = calc_mpe_dbn(engine2, long_seq);
+  pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, chars);
+
+else
+  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', 100);
+
+  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);
+    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
+
+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,:))
+squeeze(A(:,2,:))
+CPDQ2.startprob
+ 
+if 0
+S=struct(CPDF2.sub_CPD_term);
+S.nsamples
+reshape(S.counts, [2 4 2])
+end
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m
new file mode 100644
index 00000000..608b6784
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m
@@ -0,0 +1,183 @@
+function bnet = mk_square_hhmm(discrete_obs, true_params, topright)
+
+% Make a 3 level  HHMM described by the following grammar
+%
+% Square -> CLK | CCK % clockwise or counterclockwise
+% CLK -> LR UD RL DU start on top left (1 2 3 4)
+% CCK -> RL UD LR DU  if start at top right (3 2 1 4)
+% CCK -> UD LR DU RL if start at top left (2 1 4 3)
+%
+% LR = left-right, UD = up-down, RL = right-left, DU = down-up
+% LR, UD, RL, DU are sub HMMs.
+%
+% For discrete observations, the subHMMs are 2-state left-right.
+% LR emits L then l, etc.
+%
+% For cts observations, the subHMMs are 1 state.
+% LR emits a vector in the -> direction, with a little noise.
+% Since there is no constraint that we remain in the LR state as long as the RL state,
+% the sides of the square might have different lengths,
+% so the result is not really a square!
+%
+% If true_params = 0, we use random parameters at the top 2 levels
+% (ready for learning). At the bottom level, we use noisy versions
+% of the "true" observations.
+%
+% If topright=1, counter-clockwise starts at top right, not top left
+% This example was inspired by Ivanov and Bobick.
+
+if nargin < 3, topright = 1; end
+
+if 1 % discrete_obs
+  Qsizes = [2 4 2];
+else
+  Qsizes = [2 4 1];
+end
+
+D = 3;
+Qnodes = 1:D;
+startprob = cell(1,D);
+transprob = cell(1,D);
+termprob = cell(1,D);
+
+% LEVEL 1
+
+startprob{1} = 'unif';
+transprob{1} = 'unif';
+
+% LEVEL 2
+
+if true_params
+  startprob{2} = zeros(2, 4);
+  startprob{2}(1, :) = [1 0 0 0];
+  if topright
+    startprob{2}(2, :) = [0 0 1 0];
+  else
+    startprob{2}(2, :) = [0 1 0 0];
+  end
+  
+  transprob{2} = zeros(4, 2, 4);
+  
+  transprob{2}(:,1,:) = [0 1 0 0
+		    0 0 1 0
+		    0 0 0 1
+		    0 0 0 1]; % 4->e
+  if topright
+    transprob{2}(:,2,:) = [0 0 0 1
+		    1 0 0 0
+		    0 1 0 0
+		    0 0 0 1]; % 4->e
+  else
+    transprob{2}(:,2,:) = [0 0 0 1
+		    1 0 0 0
+		    0 0 1 0 % 3->e
+		    0 0 1 0];
+  end
+  
+  %termprob{2} = 'rightstop';
+  termprob{2} = zeros(2,4,2);
+  pfin = 0.8;
+  termprob{2}(1,:,2) = [0 0 0 pfin]; % finish in state 4 (DU)
+  termprob{2}(1,:,1) = 1 - [0 0 0 pfin];
+  if topright
+    termprob{2}(2,:,2) = [0 0 0 pfin];
+    termprob{2}(2,:,1) = 1 - [0 0 0 pfin];
+  else
+    termprob{2}(2,:,2) = [0 0 pfin 0];  % finish in state 3 (RL)
+    termprob{2}(2,:,1) = 1 - [0 0 pfin 0];
+  end
+else
+  % In the unsupervised case, it is essential that we break symmetry
+  % in the initial param estimates.
+  %startprob{2} = 'unif';
+  %transprob{2} = 'unif';
+  %termprob{2} = 'unif';
+  startprob{2} = 'rnd';
+  transprob{2} = 'rnd';
+  termprob{2} = 'rnd';
+end
+
+% LEVEL 3
+
+if 1 |  true_params
+  startprob{3} = 'leftstart';
+  transprob{3}  = 'leftright';
+  termprob{3} = 'rightstop';
+else
+  % If we want to be able to run a base-level model backwards...
+  startprob{3} = 'rnd';
+  transprob{3}  = 'rnd';
+  termprob{3} = 'rnd';
+end
+ 
+
+% OBS LEVEl
+
+if discrete_obs
+  % Initialise observations of lowest level primitives in a way which we can interpret
+  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');
+  Osize = length(chars);
+  
+  if true_params
+    p = 1; % makes each state fully observed
+  else
+    p = 0.9;
+  end
+  
+  obsprob = (1-p)*ones([4 2 Osize]);
+  %       Q2 Q3 O
+  obsprob(1, 1, L) =  p;
+  obsprob(1, 2, l) =  p;
+  obsprob(2, 1, U) =  p;
+  obsprob(2, 2, u) =  p;
+  obsprob(3, 1, R) =  p;
+  obsprob(3, 2, r) =  p;
+  obsprob(4, 1, D) =  p;
+  obsprob(4, 2, d) =  p;
+  obsprob = mk_stochastic(obsprob);
+  Oargs = {'CPT', obsprob};
+else
+  % Initialise means of lowest level primitives in a way which we can interpret
+  % These means are little vectors in the east, south, west, north directions.
+  % (left-right=east, up-down=south, right-left=west, down-up=north)
+  Osize = 2;
+  mu = zeros(2, Qsizes(2), Qsizes(3));
+  scale = 3;
+  if true_params
+    noise = 0;
+  else
+    noise = 0.5*scale;
+  end
+  for q3=1:Qsizes(3)
+    mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1);
+  end
+  for q3=1:Qsizes(3)
+    mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1);
+  end
+  for q3=1:Qsizes(3)
+    mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1);
+  end
+  for q3=1:Qsizes(3)
+    mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1);
+  end
+  Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]);
+  Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'};
+end
+
+if discrete_obs
+  selfprob = 0.5;
+else
+  selfprob = 0.95;
+  % If less than this, it won't look like a square
+  % because it doesn't spend enough time in each state
+  % Unfortunately, the variance on durations (lengths of each side)
+  % is very large
+end
+bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ...
+	       'Oargs', Oargs, 'Ops', Qnodes(2:3), 'selfprob', selfprob, ...
+	       'startprob', startprob, 'transprob', transprob, 'termprob', termprob);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m
new file mode 100644
index 00000000..e6701e45
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m
@@ -0,0 +1,32 @@
+function plot_square_hhmm(ev)
+% Plot the square shape implicit in the evidence.
+% ev{i,t} is the value of node i in slice t.
+% The observed node contains a velocity (delta increment), which is converted
+% into a position.
+% The Q2 node specifies which model is used; each segment is color-coded
+% in the order red, green, blue, black.
+
+Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6;
+
+delta = cell2num(ev(Onode,:)); % delta(:,t)
+Q2label = cell2num(ev(Q2,:)); 
+
+T = size(delta, 2);
+pos = zeros(2,T+1);
+clf
+hold on
+cols = {'r', 'g', 'b', 'k'};
+boundary = 0;
+coli = 1;
+for t=2:T+1
+  pos(:,t) = pos(:,t-1) + delta(:,t-1);
+  plot(pos(1,t), pos(2,t), sprintf('%c.', cols{coli}));
+  if t < T
+    boundary = (Q2label(t) ~= Q2label(t-1));
+  end
+  if boundary
+    coli = coli + 1;
+    coli = mod(coli-1, length(cols)) + 1;
+  end
+end
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m
new file mode 100644
index 00000000..a0f9007e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m
@@ -0,0 +1,160 @@
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+discrete_obs = 1;
+topright = 0;
+
+Qsizes = [2 4 2];
+D = 3;
+Qnodes = 1:D;
+startprob = cell(1,D);
+transprob = cell(1,D);
+termprob = cell(1,D);
+
+% LEVEL 1
+
+startprob{1} = 'ergodic';
+transprob{1} = 'ergodic';
+
+% LEVEL 2
+
+startprob{2} = zeros(2, 4);
+startprob{2}(1, :) = [1 0 0 0];
+if topright
+  startprob{2}(2, :) = [0 0 1 0];
+else
+  startprob{2}(2, :) = [0 1 0 0];
+end
+
+transprob{2} = zeros(4, 2, 4);
+
+transprob{2}(:,1,:) = [0 1 0 0
+		       0 0 1 0
+		       0 0 0 1
+		       0 0 0 1]; % 4->e
+if topright
+  transprob{2}(:,2,:) = [0 0 0 1
+		    1 0 0 0
+		    0 1 0 0
+		    0 0 0 1]; % 4->e
+else
+  transprob{2}(:,2,:) = [0 0 0 1
+		    1 0 0 0
+		    0 0 1 0 % 3->e
+		    0 0 1 0];
+end
+
+%termprob{2} = 'rightstop';
+termprob{2} = zeros(2,4,2);
+pfin = 0.8;
+termprob{2}(1,:,2) = [0 0 0 pfin]; % finish in state 4 (DU)
+termprob{2}(1,:,1) = 1 - [0 0 0 pfin];
+if topright
+  termprob{2}(2,:,2) = [0 0 0 pfin];
+  termprob{2}(2,:,1) = 1 - [0 0 0 pfin];
+else
+  termprob{2}(2,:,2) = [0 0 pfin 0];  % finish in state 3 (RL)
+  termprob{2}(2,:,1) = 1 - [0 0 pfin 0];
+end
+
+% LEVEL 3
+
+startprob{3} = 'leftstart';
+transprob{3}  = 'leftright';
+termprob{3} = 'rightstop';
+
+
+% OBS LEVEl
+
+if discrete_obs
+  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');
+  Osize = length(chars);
+  
+  obsprob = zeros([4 2 Osize]);
+  %       Q2 Q3 O
+  obsprob(1, 1, L) =  1.0;
+  obsprob(1, 2, l) =  1.0;
+  obsprob(2, 1, U) =  1.0;
+  obsprob(2, 2, u) =  1.0;
+  obsprob(3, 1, R) =  1.0;
+  obsprob(3, 2, r) =  1.0;
+  obsprob(4, 1, D) =  1.0;
+  obsprob(4, 2, d) =  1.0;
+  
+  Oargs = {'CPT', obsprob};
+else
+  Osize = 2;
+  mu = zeros(2, 4, 2);
+  noise = 0;
+  scale = 10;
+  for q3=1:2
+    mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1);
+  end
+  for q3=1:2
+    mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1);
+  end
+  for q3=1:2
+    mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1);
+  end
+  for q3=1:2
+    mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1);
+  end
+  Sigma = repmat(reshape(0.01*eye(2), [2 2 1 1 ]), [1 1 4 2]);
+  Oargs = {'mean', mu, 'cov', Sigma};
+end
+
+bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ...
+	       'Oargs', Oargs, 'Ops', Qnodes(2:3), ...
+	       'startprob', startprob, 'transprob', transprob, 'termprob', termprob);
+
+if discrete_obs
+  Tmax = 30;
+else
+  Tmax = 200;
+end
+usecell = ~discrete_obs;
+Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6;
+Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3];
+
+for seqi=1:3
+  evidence = sample_dbn(bnet, Tmax, usecell, 'stop_sampling_F2');      
+  T = size(evidence, 2)
+  if discrete_obs
+    pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, chars);
+  else
+    pos = zeros(2,T+1);
+    delta = cell2num(evidence(Onode,:));
+    clf
+    hold on
+    cols = {'r', 'g', 'k', 'b'};
+    boundary = cell2num(evidence(F3,:))-1;
+    coli = 1;
+    for t=2:T+1
+      pos(:,t) = pos(:,t-1) + delta(:,t-1);
+      plot(pos(1,t), pos(2,t), sprintf('%c.', cols{coli}));
+      if boundary(t-1)
+	coli = coli + 1;
+	coli = mod(coli-1, length(cols)) + 1;
+      end
+    end
+    %plot(pos(1,:), pos(2,:), '.')
+    %pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, []);
+    pause
+  end
+end
+
+eclass = bnet.equiv_class;
+S=struct(bnet.CPD{eclass(Q2,2)});
+
+
+
+
+
+
+