function bnet = mk_map_hhmm(varargin)% p is the prob of a successful move (defines the reliability of motors)p = 1;num_obs_nodes = 1;for i=1:2:length(varargin)  switch varargin{i},   case 'p', p = varargin{i+1};   case 'numobs', num_obs_node = varargin{i+1};  endendq = 1-p;% assign numbers to the nodes in topological orderU = 1; A = 2; C = 3; F = 4; O = 5;% create graph structuress = 5; % slice sizeintra = zeros(ss,ss);intra(U,F)=1;intra(A,[C F O])=1;intra(C,[F O])=1;inter = zeros(ss,ss);inter(U,[A C])=1;inter(A,[A C])=1;inter(F,[A C])=1;inter(C,C)=1;% node sizesns = zeros(1,ss);ns(U) = 2; % left/rightns(A) = 2;ns(C) = 3;ns(F) = 2;ns(O) = 5; % we will assign each state a unique symboll = 1; r = 2; % left/rightL = 1; R = 2;% Make the DBNbnet = mk_dbn(intra, inter, ns, 'observed', O);eclass = bnet.equiv_class;% Define CPDs for slice 1% We clamp all of them, i.e., do not try to learn them.% uniform probs over actions (the input could be chosen from a policy)bnet.CPD{eclass(U,1)} = tabular_CPD(bnet, U, 'CPT', mk_stochastic(ones(ns(U),1)), ...				    'adjustable', 0);% uniform probs over starting abstract statebnet.CPD{eclass(A,1)} = tabular_CPD(bnet, A, 'CPT', mk_stochastic(ones(ns(A),1)), ...				    'adjustable', 0);% Uniform probs over starting concrete state, modulo the fact% that corridor 2 is only of length 2.CPT = zeros(ns(A), ns(C)); % CPT(i,j) = P(C starts in j | A=i)CPT(1, :) = [1/3 1/3 1/3];CPT(2, :) = [1/2 1/2 0];bnet.CPD{eclass(C,1)} = tabular_CPD(bnet, C, 'CPT', CPT, 'adjustable', 0);% Termination probsCPT = zeros(ns(U), ns(A), ns(C), ns(F));CPT(r,1,1,:) = [1 0];CPT(r,1,2,:) = [1 0];CPT(r,1,3,:) = [q p];CPT(r,2,1,:) = [1 0];CPT(r,2,2,:) = [q p];CPT(l,1,1,:) = [q p];CPT(l,1,2,:) = [1 0];CPT(l,1,3,:) = [1 0];CPT(l,2,1,:) = [q p];CPT(l,2,2,:) = [1 0];bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', CPT);% Assign each state a unique observationCPT = zeros(ns(A), ns(C), ns(O));CPT(1,1,1)=1; CPT(1,2,2)=1;CPT(1,3,3)=1;CPT(2,1,4)=1;CPT(2,2,5)=1;%CPT(2,3,:) undefinedbnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', CPT);% Define the CPDs for slice 2% Abstract% Since the top level never resets, the starting distribution is irrelevant:% A2 will be determined by sampling from transmat(A1,:).% But the code requires we specify it anyway; we make it all 0s, a dummy value.startprob = zeros(ns(U), ns(A));transmat = zeros(ns(U), ns(A), ns(A));transmat(R,1,:) = [q p];transmat(R,2,:) = [0 1];transmat(L,1,:) = [1 0];transmat(L,2,:) = [p q];% Qps are the parents we condition the parameters on, in this case just% the past action.bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ...				  'startprob', startprob, 'transprob', transmat);% Concretetransmat = zeros(ns(C), ns(U), ns(A), ns(C));transmat(1,r,1,:) = [q p 0.0];transmat(2,r,1,:) = [0.0 q p];transmat(3,r,1,:) = [0.0 0.0 1.0];transmat(1,r,2,:) = [q p 0.0];transmat(2,r,2,:) = [0.0 1.0 0.0];%transmat(1,l,1,:) = [1.0 0.0 0.0];transmat(2,l,1,:) = [p q 0.0];transmat(3,l,1,:) = [0.0 p q];transmat(1,l,2,:) = [1.0 0.0 0.0];transmat(2,l,2,:) = [p q 0.0];% Add a new dimension for A(t-1), by copying old vals,% so the matrix is the same size as startprobtransmat = reshape(transmat, [ns(C) ns(U) ns(A) 1 ns(C)]);transmat = repmat(transmat, [1 1 1 ns(A) 1]);% startprob(C(t-1), U(t-1), A(t-1), A(t), C(t))startprob = zeros(ns(C), ns(U), ns(A), ns(A), ns(C));startprob(1,L,1,1,:) = [1.0 0.0 0.0];startprob(3,R,1,2,:) = [1.0 0.0 0.0];startprob(3,R,1,1,:) = [0.0 0.0 1.0];% startprob(1,L,2,1,:) = [0.0 0.0 010];startprob(2,L,2,1,:) = [1.0 0.0 0.0];startprob(2,R,2,2,:) = [0.0 1.0 0.0];% want transmat(U,A,C,At,Ct), ie. in topo ordertransmat = permute(transmat, [2 3 1 4 5]);startprob  = permute(startprob, [2 3 1 4 5]);bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ...				  'startprob', startprob, 'transprob', transmat);