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 1startprob{1} = 'ergodic';transprob{1} = 'ergodic';% LEVEL 2startprob{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];endtransprob{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->eif topright  transprob{2}(:,2,:) = [0 0 0 1		    1 0 0 0		    0 1 0 0		    0 0 0 1]; % 4->eelse  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 3startprob{3} = 'leftstart';transprob{3}  = 'leftright';termprob{3} = 'rightstop';% OBS LEVElif 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};endbnet = 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;endusecell = ~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  endendeclass = bnet.equiv_class;S=struct(bnet.CPD{eclass(Q2,2)});