learn_struct_tan_em.m.svn-base

bayesian network structrue learning matlab program

function dag = learn_struct_tan_EM(data, class, node_sizes, root, prior, nbloopmax, thresh)
% LEARN_STRUCT_TAN_EM 
% [bnet, Ebic] = learn_struct_mwstem(data, class, node_sizes, prior, root, nbloopmax, thresh)
%
% Input : 
%   data{i,m} a cell where the node i in the case m,
%   class is the number of the class node,
%   discrete = [ 1 if discret-node 0 if not ],       (1:N)
%   node_sizes = 1 if gaussian node,                 (max on complete samples)
%   prior = 1 to use uniform Dirichlet prior         (0)
%   root is the futur root-node of the tree T.       (random)
%   nbloopmax = max loop number                      (ceil(log(N*log(N))))
%   thresh = the convergence test's threshold        (1e-3)
%
% Output :
%	bnet = the output bayesian network
%       Ebic = the espected BIC score of bnet given the data
%

[N, m]=size(data);
log_m = log(m);
if nargin<7, thresh = 1e-3; end
max_iter = 10; % for learn_struct_params
if nargin<6, nbloopmax = ceil(log(N*log(N))), end
if nargin<5, prior = 0; end
if nargin<4, root = ceil(N*rand(1)), end
if nargin<3,
  misv = -9999;
  data_mat = bnt_to_mat(data,misv);
  node_sizes = max(data_mat'),
  clear data_mat
end

discrete = 1:N;
nbloop = 0;
variab = mysetdiff(1:N, class);

[Bbest1, Sbest, Obest] = learn_struct_mwst_EM(data(variab, :), 1:N-1, node_sizes(variab), prior, nbloopmax, thresh);
%[Bbest, Sbest, Obest] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, nbloopmax, thresh)
dag = zeros(N);
dag(variab, variab) = Bbest1.dag;
dag(root, variab) = 1;

%Bbest = mk_bnet(dag, discrete);
%for i=discrete, Bbest.CPD{i} = tabular_CPD(Bbest, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end
%engine = jtree_inf_engine(Bbest);
%Bbest = learn_params_em(engine, data, max_iter, thresh);