BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 154 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/examples/test_MWSTEM.m b/sourcecodes/bnt-master/SLP/examples/test_MWSTEM.m
new file mode 100644
index 00000000..cb4b4837
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/examples/test_MWSTEM.m
@@ -0,0 +1,154 @@
+% francois.olivier.c.h@gmail.com
+
+%ddd = datestr(now);
+%ddd([12 15 18])='-' ;
+%fnd=[ddd '.txt'];
+%diary(fnd)
+
+%dbstop if error
+
+clear all;
+close all;
+
+ rand('state',sum(100*clock))
+
+ nbloopmax = 5;        % number of loop max in MWST-EM
+
+
+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('ASIA net.');
+
+fprintf('\n============================= Test MWST-EM\n');
+
+n=8;
+m=500;
+bnet=mk_asia2_bnet;
+data = cell(n,m);
+for l = 1:m, data(:,l) = sample_bnet(bnet); end
+asiab=cell2mat(data);
+fprintf('Complete data have been created.');
+
+ 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('Missing data percentage : %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);
+
+%root = 1;
+prior = 0;
+  tmp=cputime;
+
+[BT_J11, Sbest0] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, nbloopmax);
+    tmp=cputime-tmp;
+    fprintf('\tMWST-EM algorithm spent %3.2f secondes\n',tmp);
+
+figure(1),  subplot(2,2,2), draw_graph(BT_J11.dag,names,carre,xx,yy); %,carre);
+    title('MSWT-EM');
+
+    fprintf('\n============================= Test AM-SEM\n');
+
+    G0 = zeros(N,N);
+    B0 = mk_bnet(G0, node_sizes);
+    for i=1:N
+        B0.CPD{i} = tabular_CPD(B0, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);%1, 'dirichlet_type','BDeu');
+    end
+
+    tmp=cputime;
+    max_loop = 10;
+
+    [B0, order, best_score] = learn_struct_EM(B0, data, max_loop);
+    G1 = B0.dag;
+    [xxx oo]=sort(order);
+    dag=G1(oo,oo);
+
+    tmp=cputime-tmp;
+    fprintf('\tSEM algorithm spent %3.2f secondes\n',tmp);
+
+
+    subplot(2,2,3), draw_graph(dag,names,carre,xx,yy); %,carre);
+    title('AMS-EM');
+
+    fprintf('\n============================= Test AM-SEM+T\n');
+
+    G0 = BT_J11.dag;
+    B0 = mk_bnet(G0, node_sizes);
+    for i=1:N
+        B0.CPD{i} = tabular_CPD(B0, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);%1, 'dirichlet_type','BDeu');
+    end
+
+    tmp=cputime;
+    max_loop = 10;
+
+    [B0, order, best_score] = learn_struct_EM(B0, data, max_loop);
+    G1 = B0.dag;
+    [xxx oo]=sort(order);
+    dag=G1(oo,oo);
+
+    tmp=cputime-tmp;
+    fprintf('\tSEM+T algorithm spent %3.2f secondes\n',tmp);
+
+
+    subplot(2,2,4), draw_graph(dag,names,carre,xx,yy); %,carre);
+    title('AMS-EM+T');
+
+
+%diary off