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

first hg version after svn

+
+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)});
+
+
+
+
+
+
+