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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)});
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