diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/hhmmF_CPD.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
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/CPDs/@hhmmF_CPD/hhmmF_CPD.m	Tue Feb 10 15:05:51 2015 +0000
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+function CPD = hhmmF_CPD(bnet, self, Qself, Fbelow, varargin)
+% HHMMF_CPD Make the CPD for an F node in a hierarchical HMM
+% CPD = hhmmF_CPD(bnet, self, Qself,  Fbelow, ...)
+%
+%        Qps
+%          \
+%           \
+%           Fself
+%         /   |
+%        /    |
+%       Qself Fbelow
+%
+% We assume nodes are ordered (numbered) as follows: Qps, Q, Fbelow, F
+% All nodes numbers should be from slice 1.
+%
+% If Fbelow if missing, this becomes a regular tabular_CPD.
+% Qps may be omitted.
+%
+% optional args [defaults]
+% 
+% Qps - node numbers.
+% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state wp 0.9]
+%
+% hhmmF_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc.
+%
+% We create an isolated tabular_CPD with no F parent to learn termprob
+% so we can avail of e.g., entropic or Dirichlet priors.
+%
+% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01.
+
+
+
+Qps = [];
+% get parents
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'Qps', Qps = varargin{i+1}; 
+  end
+end
+
+ns = bnet.node_sizes(:);
+Qsz = ns(Qself);
+Qpsz = prod(ns(Qps));
+CPD.Qsz = Qsz;
+CPD.Qpsz = Qpsz;
+
+ps = parents(bnet.dag, self);
+CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps);
+CPD.Qps_ndx = find_equiv_posns(Qps, ps);
+CPD.Qself_ndx = find_equiv_posns(Qself, ps);
+
+% set default arguments
+p = 0.9;
+%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz
+termprob = zeros(Qpsz, Qsz, 2);
+termprob(:, Qsz, 2) = p; 
+termprob(:, Qsz, 1) = 1-p; 
+termprob(:, 1:(Qsz-1), 1) = 1; 
+    
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'termprob', termprob = varargin{i+1}; 
+  end
+end
+
+CPD.sub_CPD_term = mk_isolated_tabular_CPD([Qpsz Qsz 2], {'CPT', termprob});
+S = struct(CPD.sub_CPD_term);
+CPD.termprob = S.CPT;
+
+CPD = class(CPD, 'hhmmF_CPD', tabular_CPD(bnet, self));
+
+CPD = update_CPT(CPD);
+