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, 112 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/qmr1.m b/sourcecodes/bnt-master/BNT/examples/static/qmr1.m
new file mode 100644
index 00000000..a618fbb6
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+++ b/sourcecodes/bnt-master/BNT/examples/static/qmr1.m
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+% Make a QMR-like network 
+% This is a bipartite graph, where the top layer contains hidden disease nodes,
+% and the bottom later contains observed finding nodes.
+% The diseases have Bernoulli CPDs, the findings noisy-or CPDs.
+% See quickscore_inf_engine for references.
+
+pMax = 0.01;
+Nfindings = 10;
+Ndiseases = 5;
+%Nfindings = 20;
+%Ndiseases = 10;
+
+N=Nfindings+Ndiseases;
+findings = Ndiseases+1:N;
+diseases = 1:Ndiseases;
+
+G = zeros(Ndiseases, Nfindings);
+for i=1:Nfindings
+  v= rand(1,Ndiseases);
+  rents = find(v<0.8);
+  if (length(rents)==0)
+    rents=ceil(rand(1)*Ndiseases);
+  end
+  G(rents,i)=1;
+end       
+
+prior = pMax*rand(1,Ndiseases);
+leak = 0.5*rand(1,Nfindings); % in real QMR, leak approx exp(-0.02) = 0.98     
+%leak = ones(1,Nfindings); % turns off leaks, which makes inference much harder
+inhibit = rand(Ndiseases, Nfindings);
+inhibit(not(G)) = 1;
+
+
+% first half of findings are +ve, second half -ve
+% The very first and last findings are hidden
+pos = 2:floor(Nfindings/2);
+neg = (pos(end)+1):(Nfindings-1);
+
+% Make the bnet in the straightforward way
+tabular_leaves = 0;
+obs_nodes = myunion(pos, neg) + Ndiseases;
+big_bnet = mk_qmr_bnet(G, inhibit, leak, prior, tabular_leaves, obs_nodes);
+big_evidence = cell(1, N);
+big_evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos)));
+big_evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg)));
+
+%clf;draw_layout(big_bnet.dag);
+%filename = '../public_html/Bayes/Figures/qmr.rnd.jpg';
+%% 3x3 inches
+%set(gcf,'units','inches');
+%set(gcf,'PaperPosition',[0 0 3 3])  
+%print(gcf,'-djpeg','-r100',filename);
+
+
+% Marginalize out hidden leaves apriori
+positive_leaves_only = 1;
+[bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, leak, prior, pos, neg, positive_leaves_only);
+obs_nodes = bnet.observed;
+evidence = cell(1, Ndiseases + length(obs_nodes));
+evidence(obs_nodes) = num2cell(vals);
+
+
+clear engine;
+engine{1} = quickscore_inf_engine(inhibit, leak, prior);
+engine{2} = jtree_inf_engine(big_bnet);
+engine{3} = jtree_inf_engine(bnet);
+
+%fname = '/home/cs/murphyk/matlab/Misc/loopybel.txt';
+global BNT_HOME
+fname = sprintf('%s/loopybel.txt', BNT_HOME);
+
+
+max_iter = 6;
+engine{4} = pearl_inf_engine(bnet, 'protocol', 'parallel', 'max_iter', max_iter);
+%engine{5} = belprop_inf_engine(bnet, 'max_iter', max_iter, 'filename', fname);
+engine{5} = belprop_inf_engine(bnet, 'max_iter', max_iter);
+
+E = length(engine);
+exact = 1:3;
+loopy = [4 5];
+
+ll = zeros(1,E);
+tic; engine{1} = enter_evidence(engine{1}, pos, neg); toc
+tic; [engine{2}, ll(2)] = enter_evidence(engine{2}, big_evidence); toc
+tic; [engine{3}, ll(3)] = enter_evidence(engine{3}, evidence); toc
+tic; [engine{4}, ll(4), niter(4)] = enter_evidence(engine{4}, evidence); toc
+tic; [engine{5}, niter(5)] = enter_evidence(engine{5}, evidence); toc
+
+ll
+
+post = zeros(E, Ndiseases);
+for e=1:E
+  for i=diseases(:)'
+    m = marginal_nodes(engine{e}, i);
+    post(e, i) = m.T(2);
+  end
+end
+
+for e=exact(:)'
+  for i=diseases(:)'
+    assert(approxeq(post(1, i), post(e, i)));
+  end
+end
+
+a = zeros(Ndiseases, 2);
+for ei=1:length(loopy)
+  for i=diseases(:)'
+    a(i,ei) = approxeq(post(1, i), post(loopy(ei), i));
+  end
+end
+disp('is the loopy posterior correct?');
+disp(a)