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, 103 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/misc/gener_data_from_bnet_miss.m b/sourcecodes/bnt-master/SLP/misc/gener_data_from_bnet_miss.m
new file mode 100644
index 00000000..3508daa0
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/misc/gener_data_from_bnet_miss.m
@@ -0,0 +1,103 @@
+function [data, comp_data, bnet_miss, taux, bnet_orig, notok, d] = gener_data_from_bnet_miss(bnet_miss, m, base_proba ,v, testdata)
+% [data, comp_data, bnet_miss, taux, bnet_orig, notok] = gener_data_from_bnet_miss(bnet_miss, m, base_proba ,v, aretestdata)
+%
+%   bnet_miss : see gener_[MCAR or MAR]_net function
+%   m : the length of the dataset
+%   if base_proba==0 or does not exist the x2_test will be passed
+%   v==1 to enter the verbose mode
+%   aretestdata==1 to always build the same dataset <-- rand('state',0)
+%
+% Francois.Olivier.C.H@gmail.com
+
+% Initialisation
+
+if nargin<5, testdata = 0; end
+if nargin<4, v = 0; end
+
+N2 = length(bnet_miss.dag); 
+if mod(N2,3)~=0, error('The number of nodes must be even in bnet_miss'); end
+N = length(bnet_miss.dag)/3;
+
+if nargin<3, base_proba=0; end
+if nargin<2, error('Not enougth parameters'); end
+
+% CHOOSE THE TEST POWER (only affect 'notok' value)
+                    chi2_0_1_1fd  =  2.705 ;
+                   chi2_0_05_1fd  =  3.841 ;
+                   chi2_0_01_1fd  =  6.635 ;
+                   chi2_0_001_1fd = 10.827 ;
+                  chi2_0_0001_1fd = 15.137 ;
+                                            choice = chi2_0_001_1fd;
+notok=0;
+clear chi2_0_1_1fd chi2_0_05_1fd chi2_0_01_1fd chi2_0_001_1fd chi2_0_0001_1fd
+
+% Recovering bnet_orig
+
+dag = bnet_miss.dag(1:N,1:N);
+bnet_orig = mk_bnet(dag, bnet_miss.node_sizes(1:N));
+CPT = CPT_from_bnet(bnet_miss, 0);
+for i=1:N, bnet_orig.CPD{i} = tabular_CPD(bnet_orig, i, CPT{i}); end
+
+% Generation of complete data
+
+if testdata, rand('state',0); randn('state',0); end
+
+data = cell(N,m);
+for l = 1:m, data(:,l) = sample_bnet(bnet_orig); end
+fprintf('Complete data have been created.');
+
+% Generation of missing array
+
+miss_array = cell(3*N,m);
+vide = cell(1,2*N); l= 1;
+while l <= m, 
+  ev(1:N) = data(:,l); ev(N+1:3*N) = vide;
+  miss_array(:,l) = sample_bnet(bnet_miss, 'evidence', ev); 
+  % apply simple rule of missingness
+  ev2 = cell2mat(miss_array(N+1:2*N, l));
+  % verification that we have not a completly missing sample
+  ev2 = 3-ev2; 
+  if prod(ev2)==1, 
+    if v, fprintf(' - %d, one completly missing sample removed', l);end
+  else l=l+1;
+  end 
+  if v, if mod(l,250)==0, fprintf('\n - %d',l); end,  end
+end
+fprintf('\n');
+
+% Generation of incomplete dataset
+
+       %%   TO REPLACE THE CELL ARRAY FOR ouput data
+       %%   WITH A MATRIX WITH A SPECIAL CASE (size+1)
+       %%   FOR MISSING DATA, SIMPLY REPLACE 1 by 0
+if 1,  %%   HERE
+  miss_array = bnt_to_mat(miss_array(N+1:2*N, :)); 
+  miss_array = 2-miss_array; 
+  data = bnt_to_mat(data);
+  comp_data = data; 
+  data = data.*miss_array; 
+  data = mat_to_bnt(data, 0); 
+else
+  comp_data = bnt_to_mat(miss_array(1:N,:),0);
+  data = bnt_to_mat(miss_array(2*N+1:3*N,:),0);
+  miss_array = bnt_to_mat(miss_array(N+1:2*N, :)); 
+  miss_array = 2-miss_array; 
+end
+fprintf('Incomplete dataset have been created.\n');
+
+% Verification of the Rate of missing data
+
+if base_proba,
+    [XX, YY]=find(miss_array==0);
+    nbr_miss = length(YY);
+    taux = nbr_miss/N/m;
+    if v, fprintf('There is %2.2f percent of missing data\n', round(taux*10000)/100); end
+
+    % Khi2 test between taux and base_proba for m*N
+    toto = m*N;
+    d = ((nbr_miss-base_proba*toto)^2)/(base_proba*toto) + (((toto-nbr_miss)-(1-base_proba)*toto)^2)/((1-base_proba)*toto);
+    if d>choice, 
+    fprintf('THE DATASET DO NOT RESPECT %2.1f%% OF MISSING DATA (%2.1f%%, Khi2 : %2.1f > %2.1f)\n', round(base_proba*10000)/100, round(taux*10000)/100, d, choice); 
+    notok = 1;
+    end
+end