Mercurial > hg > camir-aes2014
diff 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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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,156 @@ +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); + +