function [obs, hidden] = sample_pomdp(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 = sample_mdp(initial_prob, transmat, act);obs = zeros(1, len);for t=1:len  obs(t) = sample_discrete(obsmat(hidden(t),:));end