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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;
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