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, 123 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/examples/test_gener.m b/sourcecodes/bnt-master/SLP/examples/test_gener.m
new file mode 100644
index 00000000..d8d5ebb2
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/examples/test_gener.m
@@ -0,0 +1,123 @@
+%  data generation
+clear all
+close all
+
+ddd = datestr(now);
+ddd([12 15 18])='-' ;
+fnd=[ddd '.txt'];
+diary(fnd)
+
+% Metaparams
+hyp = 'mar';
+ntests = 2;   % change it !
+nessais = 2;  % change it !
+m = 1000;     % change it !
+
+% INIT
+missing = 2;
+misv = -9999;
+%test = zeros(nessais,6);
+test = zeros(nessais,ntests);
+taille = zeros(nessais,ntests);
+kiki = zeros(nessais,1);
+
+%%%%%%%%%% TESTS %%%%%%%%%%%
+for essai=1:nessais
+
+  N = ceil(rand*11) + 2
+  base_proba = .15 + rand/4;
+
+  fan_in = ceil(rand*4)
+  dag=mk_rnd_dag(N,fan_in)
+  discrete=1:N;
+  for i=1:N, node_sizes(i) = ceil(rand*4)+1; end
+  bnet = mk_bnet(dag, node_sizes, discrete);
+  node_sizes
+  for i=1:N, bnet.CPD{i} = tabular_CPD(bnet, i); end
+
+  if prod([hyp(1:3) == 'mca']+0),
+    bnet_miss = gener_MCAR_net(bnet, base_proba);
+    disp(' - MCAR net generated');
+  elseif prod([hyp(1:3) == 'mar']+0),
+    bnet_miss = gener_MAR_net(bnet, base_proba);
+    disp(' - MAR net generated');
+  end
+
+  [data, comp_data, bnet_miss, taux, bnet_orig, notok, dT] = gener_data_from_bnet_miss(bnet_miss, m, base_proba ,1);
+  base_proba
+
+  kiki(essai) = 1-chi2cdf(dT,1);
+
+  %test(essai,1)=1-chi2cdf(d,df);
+  node0=[]; param0=[];
+  for i=1:ntests
+    % choose a node
+    node=ceil(rand*N);
+    while ~isempty(myintersect(node,node0)) & i<N-2,
+      node = ceil(rand*N);
+    end
+    node0 = myunion(node0,node);
+
+    % choose a param
+    par = parents(bnet_miss.dag, node+N);
+    sorted=[]; ord=[];
+    for par1=1:length(par)
+      ord(par1)=find(par(par1)==bnet_miss.order);
+    end
+    [tmp, sorted] = sort(ord);
+    family = par(sorted);
+
+    state = []; stateM = [];
+    for par1=1:length(family)
+      state(par1) = ceil(rand*bnet_miss.node_sizes(family(par1)));
+      if state(par1) == bnet_miss.node_sizes(family(par1)), stateM(par1) = misv; else, stateM(par1) = state(par1); end
+    end
+
+    if prod([hyp(1:3) == 'mca']+0),
+      family = family-N;
+    elseif prod([hyp(1:3) == 'mar']+0),
+      family = family-2*N;
+    end
+    family(end+1)=node;
+    state(end+1) = missing; stateM(end+1)=misv;
+
+    CPT = CPT_from_bnet(bnet_miss);
+    value = CPT{node+N}(subv2ind(bnet_miss.node_sizes(family), state))
+
+    % test this param "value"
+    datamat = bnt_to_mat(data(family,:), misv);
+    indj=1:m;
+    for par1 = 1:(length(family)-1)
+      [tmp, indj] = find(datamat(par1,indj)==stateM(par1));
+    end
+    mtest = length(indj);
+    [tmp, indj] = find(datamat(length(family),indj)==stateM(length(family)));
+
+    toto=mtest; nbr_miss=length(indj);
+d = ((nbr_miss-value*toto)^2)/(value*toto)...
+  + (((toto-nbr_miss)-(1-value)*toto)^2)/((1-value)*toto)
+
+    family
+    %df = prod(bnet_miss.node_sizes(family)-1);
+    df = 1; % present or missing
+    %for chiv=1:nchi,
+    %  if d>chi2_table(chi2(chiv),df), test(essai,i,chiv) = 1; end
+    %end
+    cumchi=chi2cdf(d,df)
+    test(essai,i) = 1-cumchi;
+    taille(essai,i) = toto;
+
+    %fprintf('%2.1f%% OF MISSING DATA (%2.1f%%, Khi2 : %2.1f > %2.1f)\n', round(base_proba*10000)/100, round(taux*10000)/100, d, chi2(nchi));
+
+  end
+  clear bnet_miss bnet datamat data node0 family node_sizes CPT indj tmp
+end
+
+kiki
+taille
+test
+
+%eval(['save resgener' ddd 'kiki taille test hyp ntests nessais = 50 m'])
+
+diary off
+