function [dag, best_score, cache] = learn_struct_gs2(data, nodesizes, seeddag, varargin) % % LEARN_STRUCT_GS2(data,seeddag) learns a structure of Bayesian net by Greedy Search. % dag = learn_struct_gs(data, nodesizes, seeddag) % % dag: the final structure matrix % Data : training data, data(i,m) is the m obsevation of node i % Nodesizes: the size array of different nodes % seeddag: given seed Dag for hill climbing, optional % cache : data structure used to memorize local score computations % (cf. SCORE_INIT_CACHE function) % % by Gang Li @ Deakin University (gli73@hotmail.com) % (use mk_nbrs_of_dag_topo, developped by Wei Hu, instead of mk_nbrs_of_dag) % (Caching implementation : ofrancois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr) % [N ncases] = size(data); if (nargin < 3 ) seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function elseif ~acyclic(seeddag) seeddag = mk_rnd_dag(N); %zeros(N,N); end; % set default params scoring_fn = 'bic'; verbose = 'yes'; cache=[]; % get params args = varargin; nargs = length(args); if length(args) > 0 if isstr(args{1}) for i = 1:2:nargs switch args{i} case 'scoring_fn', scoring_fn = args{i+1}; case 'verbose', verbose = strcmp(args{i+1},'yes'); case 'cache', cache=args{i+1} ; end; end; end; end; done = 0; [best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); while ~done [dags,op,nodes] = mk_nbrs_of_dag_topo(seeddag); nbrs = length(dags); [scores cache] = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn,'cache',cache); max_score = max(scores); new = find(scores == max_score ); if ~isempty(new) & (max_score > best_score) p = sample_discrete(normalise(ones(1, length(new)))); best_score = max_score; seeddag = dags{new(p)}; else done = 1; end; end; dag = seeddag; outcount = 0; [best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); while outcount < 2 innercount = 0; for i=1:N for j=1:N if i==j, continue; end; if seeddag(i,j) == 0 % No edge i-->j, then try to add it tempdag = seeddag; tempdag(i,j) = 1; if acyclic(tempdag) [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); if temp_score > best_score seeddag = tempdag best_score= temp_score; innercount = innercount +1; end; end else % exists edge i--j, then try reverse it or remove it tempdag = seeddag; tempdag(i,j) = 0; tempdag(j,i) = 1; if acyclic(tempdag) [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); if temp_score > best_score seeddag = tempdag; best_score = temp_score; innercount = innercount +1; else tempdag = seeddag; tempdag(i,j) = 0; [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); if temp_score > best_score seeddag = tempdag; best_score= temp_score; innercount = innercount +1; end; end; else tempdag = seeddag; tempdag(i,j)=0; [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache); if temp_score > best_score seeddag = tempdag best_score= temp_score; innercount = innercount +1; end; end; end; end; % end for j end; % end for i if innercount == 0 outcount = outcount +1; end; end; % end while dag = seeddag;