Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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}; end end q = 1-p; % assign numbers to the nodes in topological order U = 1; A = 2; C = 3; F = 4; O = 5; % create graph structure ss = 5; % slice size intra = 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 sizes ns = zeros(1,ss); ns(U) = 2; % left/right ns(A) = 2; ns(C) = 3; ns(F) = 2; ns(O) = 5; % we will assign each state a unique symbol l = 1; r = 2; % left/right L = 1; R = 2; % Make the DBN bnet = 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 state bnet.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 probs CPT = 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 observation CPT = 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,:) undefined bnet.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); % Concrete transmat = 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 startprob transmat = 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 order transmat = 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);