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Diffstat (limited to 'sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m')
| -rw-r--r-- | sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m | 56 |
1 files changed, 56 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m new file mode 100644 index 00000000..e32dee80 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_hc.m @@ -0,0 +1,56 @@ +function [dag,best_score] = learn_struct_hc(data, nodesizes, seeddag, varargin) +% +% LEARN_STRUCT_HC(data,seeddag) learns a structure of Bayesian net by Hill Climbing. +% dag = learn_struct_hc(data, nodesizes, seeddag) +% +% dag: the final structurre 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 +% +% by Gang Li @ Deakin University (gli73@hotmail.com) + +[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'; + +% 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'); + end; + end; + end; +end; + +done = 0; +best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn); +while ~done + [dags,op,nodes] = mk_nbrs_of_dag(seeddag); + nbrs = length(dags); + scores = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn); + 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; + |