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, 172 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m
new file mode 100644
index 00000000..2fa81ac6
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m
@@ -0,0 +1,172 @@
+function dag = learn_struct_tan_EM(data, class, node_sizes, root, prior, nbloopmax, thresh)
+% LEARN_STRUCT_TAN_EM 
+% dag = learn_struct_tan_EM(data, class, node_sizes, root, prior, nbloopmax, thresh)
+%
+% Learn TAN classifier for discrete variables from incomplete dataset
+%
+% Input : 
+%   data{i,m} a cell where the node i in the case m,
+%   class is the number of the class node,
+%   node_sizes = 1 if gaussian node,                 (max on complete samples)
+%   root is the futur root-node of the tree T.       (random)
+%   prior = 1 to use uniform Dirichlet prior         (0)
+%   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
+%
+% francois.olivier.c.h@gmail.com
+
+[N, m]=size(data);
+log_m = log(m);
+if nargin<7, thresh = 1e-4; end
+max_iter = 10; % for learn_struct_params
+if nargin<6, nbloopmax = 15, 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_EM4tan(data(variab, :), 1:N-1, node_sizes(variab), prior, nbloopmax, thresh);
+dag = zeros(N);
+dag(variab, variab) = Bbest1.dag;
+dag(class, 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);
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+function [Bbest, Sbest, Obest] = learn_struct_mwst_EM4tan(data, discrete, node_sizes, prior, nbloopmax, thresh)
+
+%fprintf('-- INITIALIZATION\n');
+%%%%%%%%%%%%%
+[N, m]=size(data);
+ rand('state',sum(100*clock))
+
+if nargin<6, thresh = 1e-4; end
+if nargin<5, nbloopmax = 15; end
+if nargin<4, prior = 0; end
+if nargin<3, 
+    misv = -9999;
+    data_mat = bnt_to_mat(data,misv);
+    node_sizes = max(data_mat'), 
+end
+if nargin<2, discrete = ones(1,N); end
+
+nbloop = 1; last=0;
+max_iter = 15; % for learn_struct_params
+
+%%%%%%%%%%%%%
+% Random Chain like DAG
+T = diag(ones(N-1,1),1); T=T+T';
+order=randperm(N); root=randperm(N); root=root(1);
+[tmp order2]=sort(order); order2;
+T2 = full(mk_rooted_tree(T,order2(root)));
+torder=topological_sort(T2(order2,order2));
+[tmp torder2]=sort(torder); torder2;
+
+bnet0 = mk_bnet(T2(order2(torder),order2(torder)), node_sizes(torder));
+for i=1:N, bnet0.CPD{i} = tabular_CPD(bnet0, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end % a priori -> change les espected counts
+
+%%%%%%%%%%%%%
+Sbest = -Inf; Bbest = bnet0; fini=0;
+
+while not(fini)
+    %fprintf('Loop %d : learning parameters...\n',nbloop);
+    engine0=jtree_inf_engine(bnet0);  
+    [bnet1, LL1, engine1] = learn_params_em(engine0, data(torder,:), max_iter, thresh);
+
+    BIC0=0;
+    for i=1:N, 
+        xxx=struct(bnet1.CPD{i}); 
+        BIC0=BIC0+bic_score_family(xxx.counts, xxx.CPT, xxx.nsamples);
+    end
+
+    if BIC0 < Sbest+ thresh*abs(Sbest),
+        fini=1; 
+    else
+        Sbest = BIC0; 
+        Bbest = bnet1;  
+        Obest = torder2 ;
+    %%%%%%%%%%%%%
+
+    theta_Xi=cell(N,1);
+    evidence = cell(1,N);
+    [engine2, loglik] = enter_evidence(engine1, evidence);
+    for j=1:N,
+        SS= marginal_nodes (engine2,torder2(j)); 
+        theta_Xi{j} = SS.T; 
+    end
+
+    theta_Xj_given_Xi = cell(N,N); 
+    for i=1:N
+        for vali = 1:node_sizes(i)
+            evidence = cell(1,N);  evidence{torder2(i)} = vali;
+            [engine2, loglik] = enter_evidence(engine1, evidence);
+            for j=mysetdiff(1:N,i)
+                SS= marginal_nodes (engine2,torder2(j)); 
+                theta_Xj_given_Xi{j,i} = [theta_Xj_given_Xi{j,i}, SS.T];
+            end
+        end
+    end
+
+    BIC_mat=zeros(N,N);
+    for i=1:N,
+        BIC_mat(i,i)=bic_score_family(theta_Xi{i}*m,theta_Xi{i},m);
+        for j=mysetdiff(1:N,i)
+            theta_XjXi=(ones(node_sizes(i),1)*theta_Xi{j}').*theta_Xj_given_Xi{i,j};
+            BIC_mat(i,j)= bic_score_family(m*theta_XjXi,theta_Xj_given_Xi{i,j},m);
+        end
+    end
+    BIC_delta = BIC_mat-diag(BIC_mat)*ones(1,N);
+
+    BIC1=0;
+    for i = 1:N
+        j = find(bnet0.dag(:,i)==1);
+        if isempty(j)
+            BIC1 = BIC1 + BIC_mat(torder2(i),torder2(i));
+        else
+            BIC1 = BIC1 + BIC_mat(torder2(i),torder2(j));
+        end
+    end
+
+        %fprintf('     Creation of the new bnet                           ');tic
+        %%%%%%%%%%%%%  
+        T2 = minimum_spanning_tree(-BIC_delta);
+        root=randperm(N); root=root(1);
+        T3 = full(mk_rooted_tree(T2, root));
+        torder=topological_sort(T3);
+        [tmp torder2]=sort(torder); torder2;
+
+        bnet0 = mk_bnet(T3(torder,torder), node_sizes(torder));
+        %BIC=0;
+        for i = 1:N
+            j = find(T3(:,i)==1);
+            if isempty(j)
+                bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xi{i}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif');
+                %BIC = BIC + BIC_mat(i,i);
+            else
+                bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xj_given_Xi{i,j}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif');
+                %BIC=BIC + BIC_mat(i,j);
+            end
+        end
+        nbloop=nbloop+1;
+        fprintf('================================================================================\n');
+        if nbloop>nbloopmax
+            fini=1;
+        end
+    end
+end