Mauch MIREX 2010

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);