testnaif.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;
disp('Jouet3 1');
N=5;
dagO = zeros(N);
dagO(1,2)=1;
dagO(2,[3 4])=1;
dagO(4,5)=1;
dagO(3,5)=1;
draw_graph(dagO);

node_sizes=2*ones(1,N);
bnetO = mk_bnet(dagO, node_sizes);
bnetO.CPD{1} = tabular_CPD(bnetO, 1, [0.2 0.8]);
bnetO.CPD{2} = tabular_CPD(bnetO, 2, [0.3 0.6 0.7 0.4]);
bnetO.CPD{3} = tabular_CPD(bnetO, 3, [0.3 0.5 0.7 0.5]);
bnetO.CPD{4} = tabular_CPD(bnetO, 4, [0.35 0.45 0.65 0.55]);
%bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.75 0.85]);
bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.25 0.60 0.75 0.85 0.75 0.40]);

m = 1000;
for l=1:m, dataO(:,l) = sample_bnet(bnetO); end

DM = 0.1
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);

nbloopmax = 5;
root = 1;
prior = 0;
discrete = 1:N;
%profile on
tic
[BT_J11, Sbest0, L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
toc
%profile off
%profile report mwstem_Jouet3_1
BT_J11.dag

BICT = get_BIC(BT_J11, data)
BICT2 = get_BICL(BT_J11, L(end) ,m)

% bnet_vide = mk_bnet(zeros(N),node_sizes);
% for i=1:N, bnet_vide.CPD{i} = tabular_CPD(bnet_vide, i); end
% %profile on
% tic
% [BS_J11, orderSEM0, BIC_scoreSEM0, L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% toc
% %profile off
% %profile report sem_Jouet3_1
% BS_J11.dag
% 
% BICS = get_BIC(BS_J11, data)
% BICS2 = get_BICL(BS_J11, L(end) ,m)
% 
% %profile on
% tic
% [BTS_J11, orderMWSTSEM0, BIC_scoreMWSTSEM0, L] = learn_struct_EM(BT_J11, data, nbloopmax*3);
% toc
% %profile off
% %profile report sem_plus_t_Jouet3_1
% BTS_J11.dag
% 
% BICTS = get_BIC(BTS_J11, data)
% BICTS2 = get_BICL(BTS_J11, L(end) ,m)
% save jouet3_1

% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% clear all;
% close all;
% disp('Jouet3 2');
% N=5;
% dagO = zeros(N);
% dagO(1,2)=1;
% dagO(2,[3 4])=1;
% dagO(4,5)=1;
% dagO(3,5)=1;
% draw_graph(dagO);
% 
% node_sizes=2*ones(1,N);
% bnetO = mk_bnet(dagO, node_sizes);
% bnetO.CPD{1} = tabular_CPD(bnetO, 1, [0.2 0.8]);
% bnetO.CPD{2} = tabular_CPD(bnetO, 2, [0.1 0.6 0.9 0.4]);
% bnetO.CPD{3} = tabular_CPD(bnetO, 3, [0.3 0.5 0.7 0.5]);
% bnetO.CPD{4} = tabular_CPD(bnetO, 4, [0.35 0.05 0.65 0.95]);
% %bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.75 0.85]);
% bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.05 0.60 0.75 0.85 0.95 0.40]);
% 
% m = 500;
% for l=1:m, dataO(:,l) = sample_bnet(bnetO); end
% 
% DM = 0.2
% 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);
% 
% nbloopmax = 5;
% root = 1;
% prior = 0;
% discrete = 1:N;
% profile on
% tic
% [BT_J12, Sbest0, L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% toc
% profile off
% profile report mwstem_Jouet3_2
% BT_J12.dag
% 
% BICT = get_BIC(BT_J12, data)
% BICT2 = get_BICL(BT_J12, L(end) ,m)
% 
% bnet_vide = mk_bnet(zeros(N),node_sizes);
% for i=1:N, bnet_vide.CPD{i} = tabular_CPD(bnet_vide, i); end
% profile on
% tic
% [BS_J12, orderSEM0, BIC_scoreSEM0, L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% toc
% profile off
% profile report sem_Jouet3_2
% BS_J12.dag
% 
% BICS = get_BIC(BS_J12, data)
% BICS2 = get_BICL(BS_J12, L(end) ,m)
% 
% profile on
% tic
% [BTS_J12, orderMWSTSEM0, BIC_scoreMWSTSEM0, L] = learn_struct_EM(BT_J12, data, nbloopmax*3);
% toc
% profile off
% profile report sem_plus_t_Jouet3_2
% BTS_J12.dag
% 
% BICTS = get_BIC(BTS_J12, data)
% BICTS2 = get_BICL(BTS_J12, L(end) ,m)
% save jouet3_2
% 
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% clear all;
% close all;
% disp('Jouet3 3');
% N=5;
% dagO = zeros(N);
% dagO(1,2)=1;
% dagO(2,[3 4])=1;
% dagO(4,5)=1;
% dagO(3,5)=1;
% draw_graph(dagO);
% 
% node_sizes=2*ones(1,N);
% bnetO = mk_bnet(dagO, node_sizes);
% bnetO.CPD{1} = tabular_CPD(bnetO, 1, [0.2 0.8]);
% bnetO.CPD{2} = tabular_CPD(bnetO, 2, [0.1 0.6 0.9 0.4]);
% bnetO.CPD{3} = tabular_CPD(bnetO, 3, [0.3 0.5 0.7 0.5]);
% bnetO.CPD{4} = tabular_CPD(bnetO, 4, [0.35 0.05 0.65 0.95]);
% %bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.75 0.85]);
% bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.05 0.60 0.75 0.85 0.95 0.40]);
% 
% m = 500;
% for l=1:m, dataO(:,l) = sample_bnet(bnetO); end
% 
% DM = 0.3
% 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);
% 
% nbloopmax = 5;
% root = 1;
% prior = 0;
% discrete = 1:N;
% profile on
% tic
% [BT_J13, Sbest03, L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% toc
% profile off
% profile report mwstem_Jouet3_3
% BT_J13.dag
% 
% BICT = get_BIC(BT_J13, data)
% BICT2 = get_BICL(BT_J13, L(end) ,m)
% 
% bnet_vide = mk_bnet(zeros(N),node_sizes);
% for i=1:N, bnet_vide.CPD{i} = tabular_CPD(bnet_vide, i); end
% profile on
% tic
% [BS_J13, orderSEM03, BIC_scoreSEM03, L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% toc
% profile off
% profile report sem_Jouet3_3
% BS_J13.dag
% 
% BICS = get_BIC(BS_J13, data)
% BICS2 = get_BICL(BS_J13, L(end) ,m)
% 
% profile on
% tic
% [BTS_J13, orderMWSTSEM03, BIC_scoreMWSTSEM03, L] = learn_struct_EM(BT_J13, data, nbloopmax*3);
% toc
% profile off
% profile report sem_plus_t_Jouet3_3
% BTS_J13.dag
% 
% BICTS = get_BIC(BTS_J13, data)
% BICTS2 = get_BICL(BTS_J13, L(end) ,m)
% save jouet3_3
% 
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Jouet2 1');
% %  N=6;
% %  dagO = zeros(N);
% %  dagO(1,[2,3])=1;
% %  dagO(2,4)=1;
% %  dagO(3,[5 6])=1;
% %  draw_graph(dagO);
% %  
% %  node_sizes=2*ones(1,N);
% %  bnetO = mk_bnet(dagO, node_sizes);
% %  bnetO.CPD{1} = tabular_CPD(bnetO, 1, [0.2 0.8]);
% %  bnetO.CPD{2} = tabular_CPD(bnetO, 2, [0.1 0.6 0.9 0.4]);
% %  bnetO.CPD{3} = tabular_CPD(bnetO, 3, [0.3 0.5 0.7 0.5]);
% %  bnetO.CPD{4} = tabular_CPD(bnetO, 4, [0.35 0.05 0.65 0.95]);
% %  bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.75 0.85]);
% %  bnetO.CPD{6} = tabular_CPD(bnetO, 6, [0.02 0.12 0.98 0.88]);
% %  
% %  m = 500;
% %  for l=1:m, dataO(:,l) = sample_bnet(bnetO); end
% %  
% %  DM = 0.1
% %  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);
% %  
% %  nbloopmax = 5;
% %  root = 1;
% %  prior = 0;
% %  discrete = 1:N;
% %  profile on
% %  tic
% %  [BT_J21, Sbest7,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% %  toc
% %  profile off
% %  profile report mwstem_jouet2_1
% %  BT_J21.dag
% %  
% %  BICT = get_BIC(BT_J21, data)
% %  BICT2 = get_BICL(BT_J21, L(end) ,m)
% %  
% %  bnet_vide = mk_bnet(zeros(N),node_sizes);
% %  for i=1:N, bnet_vide.CPD{i} = tabular_CPD(bnet_vide, i); end
% %  profile on
% %  tic
% %  [BS_J21, orderSEM7, BIC_scoreSEM7,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4)
% %  toc
% %  profile off
% %  profile report sem_jouet2_1
% %  BS_J21.dag
% %  
% %  BICS = get_BIC(BS_J21, data)
% %  BICS2 = get_BICL(BS_J21, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_J21, orderMWSTSEM7, BIC_scoreMWSTSEM7,L] = learn_struct_EM(BT_J21, data, nbloopmax*3)
% %  toc
% %  profile off
% %  profile report sem_plus_t_jouet2_1
% %  BTS_J21.dag
% %  
% %  BICTS = get_BIC(BTS_J21, data)
% %  BICTS2 = get_BICL(BTS_J21, L(end) ,m)
% %  save jouet2_1
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Jouet2 2');
% %  N=6;
% %  dagO = zeros(N);
% %  dagO(1,[2,3])=1;
% %  dagO(2,4)=1;
% %  dagO(3,[5 6])=1;
% %  draw_graph(dagO);
% %  
% %  node_sizes=2*ones(1,N);
% %  bnetO = mk_bnet(dagO, node_sizes);
% %  bnetO.CPD{1} = tabular_CPD(bnetO, 1, [0.2 0.8]);
% %  bnetO.CPD{2} = tabular_CPD(bnetO, 2, [0.1 0.6 0.9 0.4]);
% %  bnetO.CPD{3} = tabular_CPD(bnetO, 3, [0.3 0.5 0.7 0.5]);
% %  bnetO.CPD{4} = tabular_CPD(bnetO, 4, [0.35 0.05 0.65 0.95]);
% %  bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.75 0.85]);
% %  bnetO.CPD{6} = tabular_CPD(bnetO, 6, [0.02 0.12 0.98 0.88]);
% %  
% %  m = 500;
% %  for l=1:m, dataO(:,l) = sample_bnet(bnetO); end
% %  
% %  DM = 0.2
% %  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);
% %  
% %  nbloopmax = 5;
% %  root = 1;
% %  prior = 0;
% %  discrete = 1:N;
% %  profile on
% %  tic
% %  [BT_J22, Sbest7,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% %  toc
% %  profile off
% %  profile report mwstem_jouet2_2
% %  BT_J22.dag
% %  
% %  BICT = get_BIC(BT_J22, data)
% %  BICT2 = get_BICL(BT_J22, L(end) ,m)
% %  
% %  bnet_vide = mk_bnet(zeros(N),node_sizes);
% %  for i=1:N, bnet_vide.CPD{i} = tabular_CPD(bnet_vide, i); end
% %  profile on
% %  tic
% %  [BS_J22, orderSEM7, BIC_scoreSEM7,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_jouet2_2
% %  BS_J22.dag
% %  
% %  BICS = get_BIC(BS_J22, data)
% %  BICS2 = get_BICL(BS_J22, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_J22, orderMWSTSEM7, BIC_scoreMWSTSEM7,L] = learn_struct_EM(BT_J22, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_jouet2_2
% %  BTS_J22.dag
% %  
% %  BICTS = get_BIC(BTS_J22, data)
% %  BICTS2 = get_BICL(BTS_J22, L(end) ,m)
% %  save jouet2_2
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Jouet2 3');
% %  N=6;
% %  dagO = zeros(N);
% %  dagO(1,[2,3])=1;
% %  dagO(2,4)=1;
% %  dagO(3,[5 6])=1;
% %  draw_graph(dagO);
% %  
% %  node_sizes=2*ones(1,N);
% %  bnetO = mk_bnet(dagO, node_sizes);
% %  bnetO.CPD{1} = tabular_CPD(bnetO, 1, [0.2 0.8]);
% %  bnetO.CPD{2} = tabular_CPD(bnetO, 2, [0.1 0.6 0.9 0.4]);
% %  bnetO.CPD{3} = tabular_CPD(bnetO, 3, [0.3 0.5 0.7 0.5]);
% %  bnetO.CPD{4} = tabular_CPD(bnetO, 4, [0.35 0.05 0.65 0.95]);
% %  bnetO.CPD{5} = tabular_CPD(bnetO, 5, [0.25 0.15 0.75 0.85]);
% %  bnetO.CPD{6} = tabular_CPD(bnetO, 6, [0.02 0.12 0.98 0.88]);
% %  
% %  m = 500;
% %  for l=1:m, dataO(:,l) = sample_bnet(bnetO); end
% %  
% %  DM = 0.3
% %  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);
% %  
% %  nbloopmax = 5;
% %  root = 1;
% %  prior = 0;
% %  discrete = 1:N;
% %  profile on
% %  tic
% %  [BT_J23, Sbest7,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% %  toc
% %  profile off
% %  profile report mwstem_jouet2_3
% %  BT_J23.dag
% %  
% %  BICT = get_BIC(BT_J23, data)
% %  BICT2 = get_BICL(BT_J23, L(end) ,m)
% %  
% %  bnet_vide = mk_bnet(zeros(N),node_sizes);
% %  for i=1:N, bnet_vide.CPD{i} = tabular_CPD(bnet_vide, i); end
% %  profile on
% %  tic
% %  [BS_J23, orderSEM7, BIC_scoreSEM7,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_jouet2_3
% %  BS_J23.dag
% %  
% %  BICS = get_BIC(BS_J23, data)
% %  BICS2 = get_BICL(BS_J23, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_J23, orderMWSTSEM7, BIC_scoreMWSTSEM7,L] = learn_struct_EM(BT_J23, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_jouet2_3
% %  BTS_J23.dag
% %  
% %  BICTS = get_BIC(BTS_J23, data)
% %  BICTS2 = get_BICL(BTS_J23, L(end) ,m)
% %  save jouet2_3
% %  
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Australian 1');
% %  load austr
% %  class_node=15;
% %  rand('state',0);randn('state',0);
% %  melange=1:size(austr,2);
% %  app=austr(:,melange); clear austr
% %  Napp=500; %Napp=round(size(app,2)*0.7);
% %  %Ntest=size(app,2)-Napp;
% %  %test=app(:,Napp+1:size(app,2));
% %  node_sizes = max(app')
% %  
% %  DM = 0.1