diff toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_hhmm3.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /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);
+