Daniel@0: if 0
Daniel@0: % Generate some sample paths
Daniel@0: 
Daniel@0: bnet = mk_map_hhmm('p', 1);
Daniel@0: % assign numbers to the nodes in topological order
Daniel@0: U = 1; A = 2; C = 3; F = 4; O = 5;
Daniel@0: 
Daniel@0: 
Daniel@0: seed = 0;
Daniel@0: rand('state', seed);
Daniel@0: randn('state', seed);
Daniel@0: 
Daniel@0: % control policy = sweep right then left
Daniel@0: T = 10;
Daniel@0: ss = 5;
Daniel@0: ev = cell(ss, T);
Daniel@0: ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]);
Daniel@0: 
Daniel@0: % fix initial conditions to be in left most state
Daniel@0: ev{A,1} = 1; 
Daniel@0: ev{C,1} = 1; 
Daniel@0: evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)
Daniel@0: 
Daniel@0: 
Daniel@0: % Now do same but with noisy actuators
Daniel@0: 
Daniel@0: bnet = mk_map_hhmm('p', 0.8);
Daniel@0: evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)
Daniel@0: 
Daniel@0: end
Daniel@0: 
Daniel@0: % Now do same but with 4 observations per slice
Daniel@0: 
Daniel@0: bnet = mk_map_hhmm('p', 0.8, 'obs_model', 'four');
Daniel@0: ss = bnet.nnodes_per_slice;
Daniel@0: 
Daniel@0: ev = cell(ss, T);
Daniel@0: ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]);
Daniel@0: ev{A,1} = 1; 
Daniel@0: ev{C,1} = 1; 
Daniel@0: evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)