Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_hhmm3.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/Old/mk_hhmm3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function bnet = mk_hhmm3(varargin) +% MK_HHMM3 Make a 3 level Hierarchical HMM +% bnet = mk_hhmm3(...) +% +% 3-layer hierarchical HMM where level 1 only connects to level 2, not 3 or obs. +% This enforces sub-models (which differ only in their Q1 index) to be shared. +% Also, we enforce the fact that each model always starts in its initial state +% and only finishes in its final state. However, the prob. of finishing (as opposed to +% self-transitioning to the final state) can be learned. +% The fact that we always finish from the same state means we do not need to condition +% F(i) on Q(i-1), since finishing prob is indep of calling context. +% +% The DBN is the same as Fig 10 in my tech report. +% +% Q1 ----------> Q1 +% | / | +% | / | +% | F2 ------- | +% | ^ \ | +% | /| \ | +% v | v v +% Q2-| --------> Q2 +% /| | ^ +% / | | /| +% | | F3 ---------/ | +% | | ^ \ | +% | v / v +% | Q3 -----------> Q3 +% | | +% \ | +% v v +% O +% +% +% Optional arguments in name/value format [default] +% +% Qsizes - sizes at each level [ none ] +% Osize - size of O node [ none ] +% discrete_obs - 1 means O is tabular_CPD, 0 means O is gaussian_CPD [0] +% Oargs - cell array of args to pass to the O CPD [ {} ] +% transprob1 - transprob1(i,j) = P(Q1(t)=j|Q1(t-1)=i) ['ergodic'] +% startprob1 - startprob1(j) = P(Q1(t)=j) ['leftstart'] +% transprob2 - transprob2(i,k,j) = P(Q2(t)=j|Q2(t-1)=i,Q1(t)=k) ['leftright'] +% startprob2 - startprob2(k,j) = P(Q2(t)=j|Q1(t)=k) ['leftstart'] +% termprob2 - termprob2(j,f) = P(F2(t)=f|Q2(t)=j) ['rightstop'] +% transprob3 - transprob3(i,k,j) = P(Q3(t)=j|Q3(t-1)=i,Q2(t)=k) ['leftright'] +% startprob3 - startprob3(k,j) = P(Q3(t)=j|Q2(t)=k) ['leftstart'] +% termprob3 - termprob3(j,f) = P(F3(t)=f|Q3(t)=j) ['rightstop'] +% +% leftstart means the model always starts in state 1. +% rightstop means the model always finished in its last state (Qsize(d)). +% +% Q1:Q3 in slice 1 are of type tabular_CPD +% Q1:Q3 in slice 2 are of type hhmmQ_CPD. +% F2 is of type hhmmF_CPD, F3 is of type tabular_CPD. + +ss = 6; D = 3; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; obs = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; +names = {'Q1', 'Q2', 'Q3', 'F3', 'F2', 'obs'}; + +intra = zeros(ss); +intra(Q1, Q2) = 1; +intra(Q2, [F2 Q3 obs]) = 1; +intra(Q3, [F3 obs]) = 1; +intra(F3, F2) = 1; + +inter = zeros(ss); +inter(Q1,Q1) = 1; +inter(Q2,Q2) = 1; +inter(Q3,Q3) = 1; +inter(F2,[Q1 Q2]) = 1; +inter(F3,[Q2 Q3]) = 1; + + +% get sizes of nodes +args = varargin; +nargs = length(args); +Qsizes = []; +Osize = 0; +for i=1:2:nargs + switch args{i}, + case 'Qsizes', Qsizes = args{i+1}; + case 'Osize', Osize = args{i+1}; + end +end +if isempty(Qsizes), error('must specify Qsizes'); end +if Osize==0, error('must specify Osize'); end + +% set default params +discrete_obs = 0; +Oargs = {}; +startprob1 = 'ergodic'; +startprob2 = 'leftstart'; +startprob3 = 'leftstart'; +transprob1 = 'ergodic'; +transprob2 = 'leftright'; +transprob3 = 'leftright'; +termprob2 = 'rightstop'; +termprob3 = 'rightstop'; + + +for i=1:2:nargs + switch args{i}, + case 'discrete_obs', discrete_obs = args{i+1}; + case 'Oargs', Oargs = args{i+1}; + case 'Q1args', Q1args = args{i+1}; + case 'Q2args', Q2args = args{i+1}; + case 'Q3args', Q3args = args{i+1}; + case 'F2args', F2args = args{i+1}; + case 'F3args', F3args = args{i+1}; + end +end + + +ns = zeros(1,ss); +ns(Qnodes) = Qsizes; +ns(obs) = Osize; +ns(Fnodes) = 2; + +dnodes = [Qnodes Fnodes]; +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; + +if strcmp(startprob1, 'ergodic') + startprob1 = normalise(ones(1,ns(Q1))); +end +if strcmp(startprob2, 'leftstart') + startprob2 = zeros(ns(Q1), ns(Q2)); + starpbrob2(:, 1) = 1.0; +end +if strcmp(startprob3, 'leftstart') + startprob3 = zeros(ns(Q2), ns(Q3)); + starpbrob3(:, 1) = 1.0; +end + +if strcmp(termprob2, 'rightstop') + p = 0.9; + termprob2 = zeros(Qsize(2),2); + termprob2(:, 2) = p; + termprob2(:, 1) = 1-p; + termprob2(1:(Qsize(2)-1), 1) = 1; +end +if strcmp(termprob3, 'rightstop') + p = 0.9; + termprob3 = zeros(Qsize(3),2); + termprob3(:, 2) = p; + termprob3(:, 1) = 1-p; + termprob3(1:(Qsize(3)-1), 1) = 1; +end + + +% SLICE 1 + +% We clamp untied nodes in the first slice, since their params can't be estimated +% from just one sequence + +bnet.CPD{eclass(Q1,1)} = tabular_CPD(bnet, Q1, 'CPT', startprob1, 'adjustable', 0); +bnet.CPD{eclass(Q2,1)} = tabular_CPD(bnet, Q2, 'CPT', startprob2, 'adjustable', 0); +bnet.CPD{eclass(Q3,1)} = tabular_CPD(bnet, Q3, 'CPT', startprob3, 'adjustable', 0); + +bnet.CPD{eclass(F2,1)} = hhmmF_CPD(bnet, F2, Qnodes, 2, D, 'termprob', termprob2); +bnet.CPD{eclass(F3,1)} = tabular_CPD(bnet, F3, 'CPT', termprob3); + +if discrete_obs + bnet.CPD{eclass(obs,1)} = tabular_CPD(bnet, obs, Oargs{:}); +else + bnet.CPD{eclass(obs,1)} = gaussian_CPD(bnet, obs, Oargs{:}); +end + +% SLICE 2 + +bnet.CPD{eclass(Q1,2)} = hhmmQ_CPD(bnet, Q1+ss, Qnodes, 1, D, 'transprob', transprob1, 'startprob', startprob1); +bnet.CPD{eclass(Q2,2)} = hhmmQ_CPD(bnet, Q2+ss, Qnodes, 2, D, 'transprob', transprob2, 'startprob', startprob2); +bnet.CPD{eclass(Q3,2)} = hhmmQ_CPD(bnet, Q3+ss, Qnodes, 3, D, 'transprob', transprob3, 'startprob', startprob3); +