comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function CPD = hhmmQ_CPD(bnet, self, varargin)
+% HHMMQ_CPD Make the CPD for a Q node in a hierarchical HMM
+% CPD = hhmmQ_CPD(bnet, self, ...)
+%
+%  Fself(t-1)   Qps(t)
+%           \    |
+%            \   v
+%  Qold(t-1) ->  Q(t)
+%            /
+%           /
+%  Fbelow(t-1) 
+%
+% Let ss = slice size = num. nodes per slice.
+% This node is Q(t), and has mandatory parents Qold(t-1) (assumed to be numbered Q(t)-ss)
+% and optional parents Fbelow, Fself, Qps.
+% We require parents to be ordered (numbered) as follows:
+% Qold, Fbelow, Fself, Qps, Q.
+%
+% If Fself=2, we use the transition matrix, else we use the prior matrix.
+% If Fself node is omitted (eg. top level), we always use the transition matrix.
+% If Fbelow=2, we may change state, otherwise we must stay in the same state.
+% If Fbelow node is omitted (eg., bottom level), we may change state at every step.
+% If Qps (Q parents) are specified, all parameters are conditioned on their joint value.
+% We may choose any subset of nodes to condition on, as long as they as numbered lower than self.
+%
+% optional args [defaults]
+%
+% Fself - node number <= ss
+% Fbelow  - node number  <= ss
+% Qps - node numbers (all <= 2*ss) - uses 2TBN indexing
+% transprob - transprob(i,k,j) = prob transition from i to j given Qps = k ['leftright']
+% selfprob  - prob of a transition from i to i given Qps=k [0.1]
+% startprob - startprob(k,j) = prob start in j given Qps = k ['leftstart']
+% startargs - other args to be passed to the sub tabular_CPD for learning startprob
+% transargs - other args will be passed to the sub tabular_CPD for learning transprob
+% fullstartprob - 1 means startprob depends on Q(t-1) [0]
+% hhmmQ_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc.
+%
+% We create isolated tabular_CPDs with no F parents to learn transprob/startprob
+% so we can avail of e.g., entropic or Dirichlet priors.
+% In the future, we will be able to represent the transprob using a tree_CPD.
+%
+% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01.
+
+
+ss = bnet.nnodes_per_slice;
+ns = bnet.node_sizes(:);
+
+% set default arguments
+Fself = [];
+Fbelow = [];
+Qps = [];
+startprob = 'leftstart';
+transprob = 'leftright';
+startargs = {};
+transargs = {};
+selfprob = 0.1;
+fullstartprob = 0;
+
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'Fself', Fself = varargin{i+1};
+   case 'Fbelow', Fbelow = varargin{i+1};
+   case 'Qps', Qps = varargin{i+1};
+   case 'transprob', transprob = varargin{i+1}; 
+   case 'selfprob',  selfprob = varargin{i+1}; 
+   case 'startprob', startprob = varargin{i+1}; 
+   case 'startargs', startargs = varargin{i+1}; 
+   case 'transargs', transargs = varargin{i+1}; 
+   case 'fullstartprob', fullstartprob = varargin{i+1}; 
+  end
+end
+
+CPD.fullstartprob = fullstartprob;
+
+ps = parents(bnet.dag, self);
+ndsz = ns(:)';
+CPD.dom_sz = [ndsz(ps) ns(self)];
+CPD.Fself_ndx = find_equiv_posns(Fself, ps);
+CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps);
+%CPD.Qps_ndx = find_equiv_posns(Qps+ss, ps);
+CPD.Qps_ndx = find_equiv_posns(Qps, ps);
+old_self = self-ss;
+CPD.old_self_ndx = find_equiv_posns(old_self, ps);
+
+Qps = ps(CPD.Qps_ndx);
+CPD.Qsz = ns(self);
+CPD.Qpsz = prod(ns(Qps));
+CPD.Qpsizes = ns(Qps);
+Qsz = CPD.Qsz;
+Qpsz = CPD.Qpsz;
+
+if strcmp(transprob, 'leftright')
+  LR = mk_leftright_transmat(Qsz, selfprob);
+  transprob = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j)
+  transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j)
+end
+transargs{end+1} = 'CPT';
+transargs{end+1} = transprob;
+CPD.sub_CPD_trans = mk_isolated_tabular_CPD(ns([old_self Qps self]), transargs);
+S = struct(CPD.sub_CPD_trans);
+%CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]);
+CPD.transprob = S.CPT;
+
+
+if strcmp(startprob, 'leftstart')
+  startprob = zeros(Qpsz, Qsz);
+  startprob(:,1) = 1;
+end
+if isempty(CPD.Fself_ndx)
+  CPD.sub_CPD_start = [];
+  CPD.startprob = [];
+else
+  startargs{end+1} = 'CPT';
+  startargs{end+1} = startprob;
+  if CPD.fullstartprob
+    CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([self Qps self]), startargs);
+    S = struct(CPD.sub_CPD_start);
+    %CPD.startprob = myreshape(S.CPT, [Qsz Qpsz Qsz]);
+    CPD.startprob = S.CPT;
+  else
+    CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([Qps self]), startargs);
+    S = struct(CPD.sub_CPD_start);
+    %CPD.startprob = myreshape(S.CPT, [CPD.Qpsizes Qsz]);
+    CPD.startprob = S.CPT;
+  end
+end
+
+CPD = class(CPD, 'hhmmQ_CPD', tabular_CPD(bnet, self));
+
+CPD = update_CPT(CPD);
+