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Diffstat (limited to 'sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m')
| -rw-r--r-- | sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m | 122 |
1 files changed, 122 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m new file mode 100644 index 00000000..e985195e --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m @@ -0,0 +1,122 @@ +function [cpdag, best_score, cache] = learn_struct_ges(data, nodesizes, varargin) +% +% LEARN_STRUCT_GES learns a structure of Bayesian net by Greedy Equivalence Search. +% cpdag = learn_struct_ges(Data, Nodesizes, 'cache', cache, 'scoring_fn', 'bic', 'verbose', 'yes') +% +% cpdag: the final cpdag +% Data : training data, data(i,m) is the m obsevation of node i +% Nodesizes: the size array of different nodes +% cache : data structure used to memorize local score computations +% (cf. SCORE_INIT_CACHE function) +% +% V1.1 : 28 july 2003 (Ph. Leray - philippe.leray@univ-nantes.fr, O. francois - francois.olivier.c.h@gmail.com) +% +% Ref: +% Optimal Structure Identification with Greedy Search, Chickering 2002 +% + +[N ncases] = size(data); +seeddag = zeros(N,N); + +% set default params +scoring_fn = 'bayesian'; +verbose = 0; +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; + +if verbose + names=cellstr(int2str((1:N)')); + carre=zeros(N,1); +end + +done = 0; +[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); +cptt=0; + +% First step : INSERT +while ~done + cptt=cptt+1; + [pdags,nodes] = mk_nbrs_of_pdag_add(seeddag); + seedold=seeddag; + sold=best_score; + nbrs = length(pdags); + dags=pdag_to_dag(pdags); + [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 = dag_to_cpdag(dags{new(p)}); + new=new(p); + if verbose + figure; + subplot(1,2,1), [xx yy]=draw_graph(seedold,names,carre); + set(gca,'color',[1 1 0]); + title(sprintf('current CPDAG (Smax=%5.2f)',sold)); + subplot(1,2,2), draw_graph(seeddag,names,carre,xx,yy); + s=sprintf(' %d',nodes{new,3}); + title([sprintf('Best in N+ = INSERT(%d, %d,',nodes{new,1},nodes{new,2}) s ')' sprintf(' S=%5.2f',max_score)]); + drawnow; + end + + else + done = 1; + end + +end; + +done = 0; +%[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache); +cptt=0; + +if sum(sum(seeddag))==0, done=1;end + +% Second step : DELETE +while ~done + cptt=cptt+1; + [pdags,nodes] = mk_nbrs_of_pdag_del(seeddag); + seedold=seeddag; sold=best_score; + nbrs = length(pdags); + dags=pdag_to_dag(pdags); + [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 = dag_to_cpdag(dags{new(p)}); + new=new(p); + if verbose + cpdags=dag_to_cpdag(dags); + figure; + subplot(1,2,1), [xx yy]=draw_graph(seedold,names,carre); + set(gca,'color',[1 1 0]); + title(sprintf('current CPDAG (Smax=%5.2f)',best_score)); + subplot(1,2,2), draw_graph(seeddag,names,carre,xx,yy); + s=sprintf('%d',nodes{new,3}); + title([sprintf('Best in N- = DELETE(%d, %d,',nodes{new,1},nodes{new,2}) s ')' sprintf(' S=%5.2f',max_score)]); + drawnow; + end + + else + done = 1; + end + +end + +cpdag = seeddag; |