www.pudn.com > HMM1.zip > pomdp_sample.m


function [obs, hidden] = pomdp_sample(initial_prob, transmat, obsmat, act)
> SAMPLE_POMDP Generate a random sequence from a Partially Observed Markov Decision Process.
> [obs, hidden] = sample_pomdp(prior, transmat, obsmat, act)
>
> Inputs:
> prior(i) = Pr(Q(1)=i)
> transmat{a}(i,j) = Pr(Q(t)=j | Q(t-1)=i, A(t)=a)
> obsmat(i,k) = Pr(Y(t)=k | Q(t)=i)
> act(a) = A(t), so act(1) is ignored
>
> Output:
> obs and hidden are vectors of length T=length(act)


len = length(act);
hidden = mdp_sample(initial_prob, transmat, act);
obs = zeros(1, len);
for t=1:len
obs(t) = sample_discrete(obsmat(hidden(t),:));
end