testmwstem.m.svn-base

bayesian network structrue learning matlab program

%ddd = datestr(now);
%ddd([12 15 18])='-' ;
%fnd=[ddd '.txt']; 
%diary(fnd)

dbstop if error

clear all;
close all;

 rand('state',sum(100*clock)) 
 
 
names={ 'A' , 'S' , 'T' , 'L' , 'B' , 'O' , 'X' , 'D' };
node = struct('visit', 1, ...
    'smoking', 2, ...
    'tuberculosis', 3, ...
    'bronchitis', 5, ...
    'lung', 4, ...
    'ou', 6, ...
    'Xray', 7, ...
    'dyspnoea', 8);

adjacency = zeros(8);
adjacency([node.visit], node.tuberculosis) = 1;
adjacency([node.smoking], node.lung) = 1;
adjacency([node.lung node.tuberculosis], node.ou) = 1;
adjacency([node.ou], node.Xray) = 1;
adjacency([node.smoking], node.bronchitis) = 1;
adjacency([node.bronchitis node.ou], node.dyspnoea) = 1;
carre=ones(1,8);

  figure(1); [xx yy] = make_layout(adjacency);
    yy=(yy-0.2)*.8/.6+.1;
    xx=(xx-0.2833)*.8/.517+.1;
    subplot(2,2,1), [xx yy]=draw_graph(adjacency,names,carre,xx,yy); %,carre);
    title('Le graphe de la Dyspnee');
   
    
fprintf('\n\n\n\n\n\n============================= Test MWST-EM\n');

 load asia2000;
 DM = 0.1
 BD0 = asiab;
 node_sizes = max(BD0');
 [N, m]=size(BD0);
 rand('state',0); randn('state',0);
 vide = rand(size(BD0))<(1-DM);
 data=BD0.*vide;
 data = mat_to_bnt(data,0);

% N=4;
% dagO = diag(ones(N-1,1),1); dag0(1,3)=1;
% figure(1), subplot(4,4,1), title('theoritical'), draw_graph(dagO);
% 
% node_sizes=2*ones(1,N);
discrete = ones(1,N);
% 
% bnetO = mk_bnet(dagO, node_sizes);
% bnetO.CPD{1} = tabular_CPD(bnetO, 1, 'CPT',[0.2 0.8]);
% bnetO.CPD{2} = tabular_CPD(bnetO, 2, 'CPT',[0.4 0.7 0.6 0.3]);
% bnetO.CPD{3} = tabular_CPD(bnetO, 3);
% bnetO.CPD{4} = tabular_CPD(bnetO, 4, 'CPT', [0.5 0.8 0.5 0.2]);
% 
% m = 1000; DM = 0.1;
% 
% for l=1:m, dataO(:,l) = sample_bnet(bnetO); end
% rand('state',0); randn('state',0);
% vide = rand(size(dataO))<(1-DM);
% data = bnt_to_mat(dataO);
% data = data.*vide;
% data = mat_to_bnt(data,0);
% clear dataO;

fprintf('Pourcentage de donn閑s manquantes : %3.1f%%\n',100*DM);

%     engine0=jtree_sparse_inf_engine(bnetO);  
%     [bnet1, LL1, engine1] = learn_params_em(engine0, data);  
% BIC0=0;
%     for i=1:N, 
%         xxx=struct(bnet1.CPD{i}); 
%         BIC0=BIC0+bic_score_family(xxx.counts, xxx.CPT, xxx.nsamples);
%     end
%     fprintf('%5.2f\n',BIC0);

nbloopmax = 6;
%root = 1;
prior = 0;
  tmp=cputime;
  
[BT_J11, Sbest0, Order2] = learn_struct_mwst_EM(data, discrete, node_sizes, prior);%, nbloopmax);
    tmp=cputime-tmp;
    fprintf('\tL''algorithme MWST-EM a dur