testnaif.m.svn-base

bayesian network structrue learning matlab program

% %  BD0 = app(:,1:Napp);
% %  [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);
% %  
% %  %node_type={'tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular'};
% %  nbloopmax = 6;
% %  root = 15;
% %  prior = 0;
% %  discrete = 1:N;
% %  profile on
% %  tic
% %  [BT_A1, Sbest8,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% %  toc
% %  profile off
% %  profile report mwstem_austr_1
% %  BT_A1.dag
% %  
% %  %BICT = get_BIC(BT_A1, data)
% %  BICT2 = get_BICL(BT_A1, 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_A1, orderSEM8, BIC_scoreSEM8,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_austr_1
% %  BS_A1.dag
% %  
% %  %BICS = get_BIC(BS_A1, data)
% %  BICS2 = get_BICL(BS_A1, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_A1, orderMWSTSEM8, BIC_scoreMWSTSEM8,L] = learn_struct_EM(BT_A1, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_austr_1
% %  BTS_A1.dag
% %  
% %  %BICTS = get_BIC(BTS_A1, data)
% %  BICTS2 = get_BICL(BTS_A1, L(end) ,m)
% %  save austr_1
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Australian 2');
% %  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 = 0.2
% %  BD0 = app(:,1:Napp);
% %  [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);
% %  
% %  %node_type={'tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular'};
% %  nbloopmax = 6;
% %  root = 15;
% %  prior = 0;
% %  discrete = 1:N;
% %  profile on
% %  tic
% %  [BT_A2, Sbest8,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% %  toc
% %  profile off
% %  profile report mwstem_austr_2
% %  BT_A2.dag
% %  
% %  %BICT = get_BIC(BT_A2, data)
% %  BICT2 = get_BICL(BT_A2, 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_A2, orderSEM8, BIC_scoreSEM8,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_austr_2
% %  BS_A2.dag
% %  
% %  %BICS = get_BIC(BS_A2, data)
% %  BICS2 = get_BICL(BS_A2, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_A2, orderMWSTSEM8, BIC_scoreMWSTSEM8,L] = learn_struct_EM(BT_A2, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_austr_2
% %  BTS_A2.dag
% %  
% %  %BICTS = get_BIC(BTS_A2, data)
% %  BICTS2 = get_BICL(BTS_A2, L(end) ,m)
% %  save austr_2
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Australian 3');
% %  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 = 0.3
% %  BD0 = app(:,1:Napp);
% %  [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);
% %  
% %  %node_type={'tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular','tabular'};
% %  nbloopmax = 6;
% %  root = 15;
% %  prior = 0;
% %  discrete = 1:N;
% %  profile on
% %  tic
% %  [BT_A3, Sbest8,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax);
% %  toc
% %  profile off
% %  profile report mwstem_austr_3
% %  BT_A3.dag
% %  
% %  %BICT = get_BIC(BT_A3, data)
% %  BICT2 = get_BICL(BT_A3, 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_A3, orderSEM8, BIC_scoreSEM8,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_austr_3
% %  BS_A3.dag
% %  
% %  %BICS = get_BIC(BS_A3, data)
% %  BICS2 = get_BICL(BS_A3, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_A3, orderMWSTSEM8, BIC_scoreMWSTSEM8,L] = learn_struct_EM(BT_A3, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_austr_3
% %  BTS_A3.dag
% %  
% %  %BICTS = get_BIC(BTS_A3, data)
% %  BICTS2 = get_BICL(BTS_A3, L(end) ,m)
% %  save austr_3
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Asia 1')
% %  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);
% %  
% %  nbloopmax = 8;
% %  root = 2;
% %  prior = 0;
% %  discrete = 1:N;
% %  thresh = 1e-3;
% %  profile on
% %  tic
% %  [BT_D1, Sbest1,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax, thresh);
% %  toc
% %  profile off
% %  profile report mwstem_asia_1
% %  BT_D1.dag
% %  
% %  BICT = get_BIC(BT_D1, data)
% %  BICT2 = get_BICL(BT_D1, 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_D1, orderSEM1, BIC_scoreSEM1,L] = learn_struct_EM(bnet_vide, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_asia_1
% %  BS_D1.dag
% %  
% %  BICS = get_BIC(BS_D1, data)
% %  BICS2 = get_BICL(BS_D1, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_D1, orderMWSTSEM1, BIC_scoreMWSTSEM1,L] = learn_struct_EM(BT_D1, data, nbloopmax*2);
% %  toc
% %  profile off
% %  profile report sem_plus_t_asia_1
% %  BTS_D1.dag
% %  
% %  BICTS = get_BIC(BTS_D1, data)
% %  BICTS2 = get_BICL(BTS_D1, L(end) ,m)
% %  save asia1
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Asia 2')
% %  load asia2000;
% %  DM = 0.2
% %  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);
% %  
% %  nbloopmax = 8;
% %  root = 2;
% %  prior = 0;
% %  discrete = 1:N;
% %  thresh = 1e-3;
% %  profile on
% %  tic
% %  [BT_D2, Sbest1,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax, thresh);
% %  toc
% %  profile off
% %  profile report mwstem_asia_2
% %  BT_D2.dag
% %  
% %  BICT = get_BIC(BT_D2, data)
% %  BICT2 = get_BICL(BT_D2, 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_D2, orderSEM1, BIC_scoreSEM1,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_asia_2
% %  BS_D2.dag
% %  
% %  BICS = get_BIC(BS_D2, data)
% %  BICS2 = get_BICL(BS_D2, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_D2, orderMWSTSEM1, BIC_scoreMWSTSEM1,L] = learn_struct_EM(BT_D2, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_asia_2
% %  BTS_D2.dag
% %  
% %  BICTS = get_BIC(BTS_D2, data)
% %  BICTS2 = get_BICL(BTS_D2, L(end) ,m)
% %  save asia2
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  disp('Asia 3')
% %  load asia2000;
% %  DM = 0.3
% %  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);
% %  
% %  nbloopmax = 8;
% %  root = 2;
% %  prior = 0;
% %  discrete = 1:N;
% %  thresh = 1e-3;
% %  profile on
% %  tic
% %  [BT_D3, Sbest1,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax, thresh);
% %  toc
% %  profile off
% %  profile report mwstem_asia_3
% %  BT_D3.dag
% %  
% %  BICT = get_BIC(BT_D3, data)
% %  BICT2 = get_BICL(BT_D3, 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_D3, orderSEM1, BIC_scoreSEM1,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_asia_3
% %  BS_D3.dag
% %  
% %  BICS = get_BIC(BS_D3, data)
% %  BICS2 = get_BICL(BS_D3, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_D3, orderMWSTSEM1, BIC_scoreMWSTSEM1,L] = learn_struct_EM(BT_D3, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_asia_3
% %  BTS_D3.dag
% %  
% %  BICTS = get_BIC(BTS_D3, data)
% %  BICTS2 = get_BICL(BTS_D3, L(end) ,m)
% %  save asia3
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  load thyroid_app.mat
% %  disp('Thyroid');
% %  data = mat_to_bnt(thyroid_app,-9999);
% %  [N, m]=size(data);
% %  prior = 0;
% %  thresh = 1e-3;
% %  discrete = 1:N;
% %  node_sizes = max(thyroid_app');
% %  
% %  root = 1;
% %  nbloopmax = 12;
% %  profile on
% %  tic
% %  [BT_T, Sbest2,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax, thresh);
% %  toc
% %  profile off
% %  profile report mwstem_thyr
% %  BT_T.dag
% %  
% %  %BICT = get_BIC(BT_T, data)
% %  BICT2 = get_BICL(BT_T, 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_T, orderSEM2, BIC_scoreSEM2,L] = learn_struct_EM(bnet_vide, data, nbloopmax*5);
% %  toc
% %  profile off
% %  profile report sem_thyr
% %  BS_T.dag
% %  
% %  %BICS = get_BIC(BS_T, data)
% %  BICS2 = get_BICL(BS_T, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_T, orderMWSTSEM2, BIC_scoreMWSTSEM2,L] = learn_struct_EM(BT_T, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_plus_t_thyr
% %  BTS_T.dag
% %  
% %  %BICTS = get_BIC(BTS_T, data)
% %  BICTS2 = get_BICL(BTS_T, L(end) ,m)
% %  save Thyr11
% %  
% %  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %  clear all;
% %  close all;
% %  load heart2
% %  disp('heart');
% %  data = BDMapp;
% %  data{5,59}=[];
% %  prior=0;
% %  root=14;
% %  nbloopmax = 12;
% %  thresh = 1e-3;
% %  m=NbApp;
% %  
% %  profile on
% %  tic
% %  [BT_H, Sbest3,L] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, root, nbloopmax, thresh);
% %  toc
% %  profile off
% %  profile report mwstem_heart
% %  BT_H.dag
% %  
% %  %BICT = get_BIC(BT_H, data)
% %  BICT2 = get_BICL(BT_H, 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_H, orderSEM3, BIC_scoreSEM3,L] = learn_struct_EM(bnet_vide, data, nbloopmax*4);
% %  toc
% %  profile off
% %  profile report sem_heart
% %  BS_H.dag
% %  
% %  %BICS = get_BIC(BS_H, data)
% %  BICS2 = get_BICL(BS_H, L(end) ,m)
% %  
% %  profile on
% %  tic
% %  [BTS_H, orderMWSTSEM3, BIC_scoreMWSTSEM3,L] = learn_struct_EM(BT_H, data, nbloopmax*3);
% %  toc
% %  profile off
% %  profile report sem_plus_t_heart
% %  BTS_H.dag
% %  
% %  %BICTS = get_BIC(BTS_H, data)
% %  BICTS2 = get_BICL(BTS_H, L(end) ,m)
% %  save heart11
% %  
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

diary off