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function CPD = tabular_CPD(bnet, self, varargin)
% TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT)
%
% CPD = tabular_CPD(bnet, node) creates a random CPT.
%
% The following arguments can be specified [default in brackets]
%
% CPT - specifies the params ['rnd']
% - T means use table T; it will be reshaped to the size of node's family.
% - 'rnd' creates rnd params (drawn from uniform)
% - 'unif' creates a uniform distribution
% adjustable - 0 means don't adjust the parameters during learning [1]
% prior_type - defines type of prior ['none']
% - 'none' means do ML estimation
% - 'dirichlet' means add pseudo-counts to every cell
% - 'entropic' means use a prior P(theta) propto exp(-H(theta)) (see Brand)
% dirichlet_weight - equivalent sample size (ess) of the dirichlet prior [1]
% dirichlet_type - defines the type of Dirichlet prior ['BDeu']
% - 'unif' means put dirichlet_weight in every cell
% - 'BDeu' means we put 'dirichlet_weight/(r q)' in every cell
% where r = self_sz and q = prod(parent_sz) (see Heckerman)
% trim - 1 means trim redundant params (rows in CPT) when using entropic prior [0]
% entropic_pcases - list of assignments to the parents nodes when we should use
% the entropic prior; all other cases will be estimated using ML [1:psz]
% sparse - 1 means use 1D sparse array to represent CPT [0]
%
% e.g., tabular_CPD(bnet, i, 'CPT', T)
% e.g., tabular_CPD(bnet, i, 'CPT', 'unif', 'dirichlet_weight', 2, 'dirichlet_type', 'unif')
%
% REFERENCES
% M. Brand - "Structure learning in conditional probability models via an entropic prior
% and parameter extinction", Neural Computation 11 (1999): 1155--1182
% M. Brand - "Pattern discovery via entropy minimization" [covers annealing]
% AI & Statistics 1999. Equation numbers refer to this paper, which is available from
% www.merl.com/reports/docs/TR98-21.pdf
% D. Heckerman, D. Geiger and M. Chickering,
% "Learning Bayesian networks: the combination of knowledge and statistical data",
% Microsoft Research Tech Report, 1994
if nargin==0
% This occurs if we are trying to load an object from a file.
CPD = init_fields;
CPD = class(CPD, 'tabular_CPD', discrete_CPD(0, []));
return;
elseif isa(bnet, 'tabular_CPD')
% This might occur if we are copying an object.
CPD = bnet;
return;
end
CPD = init_fields;
ns = bnet.node_sizes;
ps = parents(bnet.dag, self);
fam_sz = ns([ps self]);
psz = prod(ns(ps));
CPD.sizes = fam_sz;
CPD.leftright = 0;
CPD.sparse = 0;
% set defaults
CPD.CPT = mk_stochastic(myrand(fam_sz));
CPD.adjustable = 1;
CPD.prior_type = 'none';
dirichlet_type = 'BDeu';
dirichlet_weight = 1;
CPD.trim = 0;
selfprob = 0.1;
CPD.entropic_pcases = 1:psz;
% extract optional args
args = varargin;
% check for old syntax CPD(bnet, i, CPT) as opposed to CPD(bnet, i, 'CPT', CPT)
if ~isempty(args) && ~ischar(args{1})
CPD.CPT = myreshape(args{1}, fam_sz);
args = [];
end
for i=1:2:length(args)
switch args{i},
case 'CPT',
T = args{i+1};
if ischar(T)
switch T
case 'unif', CPD.CPT = mk_stochastic(myones(fam_sz));
case 'rnd', CPD.CPT = mk_stochastic(myrand(fam_sz));
otherwise, error(['invalid CPT ' T]);
end
else
CPD.CPT = myreshape(T, fam_sz);
end
case 'prior_type', CPD.prior_type = args{i+1};
case 'dirichlet_type', dirichlet_type = args{i+1};
case 'dirichlet_weight', dirichlet_weight = args{i+1};
case 'adjustable', CPD.adjustable = args{i+1};
case 'clamped', CPD.adjustable = ~args{i+1};
case 'trim', CPD.trim = args{i+1};
case 'entropic_pcases', CPD.entropic_pcases = args{i+1};
case 'sparse', CPD.sparse = args{i+1};
otherwise, error(['invalid argument name: ' args{i}]);
end
end
switch CPD.prior_type
case 'dirichlet',
switch dirichlet_type
case 'unif', CPD.dirichlet = dirichlet_weight * myones(fam_sz);
case 'BDeu', CPD.dirichlet = (dirichlet_weight/psz) * mk_stochastic(myones(fam_sz));
otherwise, error(['invalid dirichlet_type ' dirichlet_type])
end
case {'entropic', 'none'}
CPD.dirichlet = [];
otherwise, error(['invalid prior_type ' prior_type])
end
% fields to do with learning
if ~CPD.adjustable
CPD.counts = [];
CPD.nparams = 0;
CPD.nsamples = [];
else
%CPD.counts = zeros(size(CPD.CPT));
CPD.counts = zeros(prod(size(CPD.CPT)), 1);
psz = fam_sz(1:end-1);
ss = fam_sz(end);
if CPD.leftright
% For each of the Qps contexts, we specify Q elements on the diagoanl
CPD.nparams = Qps * Q;
else
% sum-to-1 constraint reduces the effective arity of the node by 1
CPD.nparams = prod([psz ss-1]);
end
CPD.nsamples = 0;
end
CPD.trimmed_trans = [];
fam_sz = CPD.sizes;
%psz = prod(fam_sz(1:end-1));
%ssz = fam_sz(end);
%CPD.trimmed_trans = zeros(psz, ssz); % must declare before reading
%sparse CPT
if CPD.sparse
CPD.CPT = sparse(CPD.CPT(:));
end
CPD = class(CPD, 'tabular_CPD', discrete_CPD(~CPD.adjustable, fam_sz));
%%%%%%%%%%%
function CPD = init_fields()
% This ensures we define the fields in the same order
% no matter whether we load an object from a file,
% or create it from scratch. (Matlab requires this.)
CPD.CPT = [];
CPD.sizes = [];
CPD.prior_type = [];
CPD.dirichlet = [];
CPD.adjustable = [];
CPD.counts = [];
CPD.nparams = [];
CPD.nsamples = [];
CPD.trim = [];
CPD.trimmed_trans = [];
CPD.leftright = [];
CPD.entropic_pcases = [];
CPD.sparse = [];
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