Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/tabular_CPD.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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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/@tabular_CPD/tabular_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +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) & ~isstr(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 = []; +