comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_hhmm2.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function bnet = mk_hhmm2(varargin)
+% MK_HHMM2 Make a 2 level Hierarchical HMM
+% bnet = mk_hhmm2(...)
+%
+% 2-layer hierarchical HMM  (node numbers in parens)
+%
+%   Q1(1) ---------> Q1(5)
+% /  | \            / |
+% |  |  v          /  |
+% |  |  F2(3) --- /   |
+% |  |  ^         \   |
+% |  | /           \  |
+% |  v              \ v
+% |  Q2(2)--------> Q2 (6)
+% |  |    
+% \  | 
+%  v v    
+%   O(4)
+%
+%
+% Optional arguments [default]
+%
+% discrete_obs - 1 means O is tabular_CPD, 0 means O is gaussian_CPD [0]
+% obsCPT       - CPT(o,q1,q2) params for O ['rnd']
+% mu           - mu(:,q1,q2) params for O [ [] ]
+% Sigma        - Sigma(:,q1,q2) params for O [ [] ]
+%
+% F2toQ1       - 1 if Q2 is an hhmm_CPD, 0 if F2 -> Q2 arc is absent, so level 2 never resets [1]
+% Q1args        - arguments to be passed to the constructors for Q1(t=2) [ {} ]
+% Q2args        - arguments to be passed to the constructors for Q2(t=2) [ {} ]
+%
+% F2 only turns on (wp 0.5) when Q2 enters its final state.
+% Q1 (slice 1) is clamped to be uniform.
+% Q2 (slice 1) is clamped to always start in state 1.
+
+[os nmodels nstates] = size(mu);
+
+ss = 4;
+Q1 = 1; Q2 = 2; F2 = 3; obs = 4;
+Qnodes = [Q1 Q2];
+names = {'Q1', 'Q2', 'F2', 'obs'};
+intra = zeros(ss);
+intra(Q1, [Q2 F2 obs]) = 1;
+intra(Q2, [F2 obs]) = 1;
+
+inter = zeros(ss);
+inter(Q1,Q1) = 1;
+inter(F2,Q1) = 1;
+if F2toQ2
+  inter(F2,Q2)=1;
+end
+inter(Q2,Q2) = 1;
+
+ns = zeros(1,ss);
+
+ns(Q1) = nmodels;
+ns(Q2) = nstates;
+ns(F2) = 2;
+ns(obs) = os;
+
+dnodes = [Q1 Q2 F2];
+if discrete_obs
+  dnodes = [dnodes obs];
+end
+onodes = [obs];
+
+bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'discrete', dnodes, 'names', names);
+eclass = bnet.equiv_class;
+
+% SLICE 1
+
+% We clamp untied nodes in the first slice, since their params can't be estimated
+% from just one sequence
+
+% uniform prior on initial model
+CPT = normalise(ones(1,nmodels));
+bnet.CPD{eclass(Q1,1)} = tabular_CPD(bnet, Q1, 'CPT', CPT, 'adjustable', 0);
+
+% each model always starts in state 1
+CPT = zeros(ns(Q1), ns(Q2));
+CPT(:, 1) = 1.0;
+bnet.CPD{eclass(Q2,1)} = tabular_CPD(bnet, Q2, 'CPT', CPT, 'adjustable', 0);
+
+% Termination probability
+CPT = zeros(ns(Q1), ns(Q2), 2);
+if 1
+  % Each model can only terminate in its final state.
+  % 0 params will remain 0 during EM, thus enforcing this constraint.
+  CPT(:, :, 1) = 1.0; % all states turn F off ...
+  p = 0.5;
+  CPT(:, ns(Q2), 2) = p; % except the last one
+  CPT(:, ns(Q2), 1) = 1-p;
+end
+bnet.CPD{eclass(F2,1)}  = tabular_CPD(bnet, F2, 'CPT', CPT);
+
+if discrete_obs
+  bnet.CPD{eclass(obs,1)} = tabular_CPD(bnet, obs, obs_args{:});
+else
+  bnet.CPD{eclass(obs,1)} = gaussian_CPD(bnet, obs, obs_args{:});
+end
+
+% SLICE 2
+
+
+bnet.CPD{eclass(Q1,2)} = hhmm_CPD(bnet, Q1+ss, Qnodes, 1, D, 'args', Q1args);
+
+if F2toQ2
+  bnet.CPD{eclass(Q2,2)} = hhmmQD_CPD(bnet, Q2+ss, Qnodes, 2, D, Q2args{:});
+else
+  bnet.CPD{eclass(Q2,2)} = tabular_CPD(bnet, Q2+ss, Q2args{:});
+end