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Diffstat (limited to 'sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m')
| -rw-r--r-- | sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m | 119 |
1 files changed, 119 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m new file mode 100644 index 00000000..4e054800 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m @@ -0,0 +1,119 @@ +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; |