Mercurial > hg > camir-aes2014
view 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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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 = [];