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, 82 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/Models/mk_minimal_qmr_bnet.m b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_minimal_qmr_bnet.m
new file mode 100644
index 00000000..99ad6e24
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_minimal_qmr_bnet.m
@@ -0,0 +1,82 @@
+function [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, leak, prior, pos, neg, pos_only)
+% MK_MINIMAL_QMR_BNET Make a QMR model which only contains the observed findings
+% [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, prior, leak, pos, neg)
+%
+% Input:
+% G(i,j) = 1 iff there is an arc from disease i to finding j
+% inhibit(i,j) = inhibition probability on i->j arc
+% leak(j) = inhibition prob. on leak->j arc
+% prior(i) = prob. disease i is on
+% pos = list of leaves that have positive observations
+% neg = list of leaves that have negative observations
+% pos_only = 1 means only include positively observed leaves in the model - the negative
+%   ones are absorbed into the prior terms
+%
+% Output:
+% bnet
+% vals is their value
+
+if pos_only
+  obs = pos;
+else
+  obs = myunion(pos, neg);
+end
+Nfindings = length(obs);
+[Ndiseases maxNfindings] = size(inhibit);
+N = Ndiseases + Nfindings;
+finding_node = Ndiseases+1:N;
+
+% j = finding_node(i) means the i'th finding node is the j'th node in the bnet
+% k = obs(i) means the i'th observed (positive) finding is the k'th finding overall
+% If all findings are observed, and posonly = 0, we have i = obs(i) for all i.
+
+%dag = sparse(N, N);
+dag = zeros(N, N);
+dag(1:Ndiseases, Ndiseases+1:N) = G(:,obs);
+
+ns = 2*ones(1,N);
+bnet = mk_bnet(dag, ns, 'observed', finding_node);
+
+CPT = cell(1, Ndiseases);
+for d=1:Ndiseases
+  CPT{d} = [1-prior(d) prior(d)];
+end
+
+if pos_only
+  % Fold in the negative evidence into the prior
+  for i=1:length(neg)
+    n = neg(i);
+    ps = parents(G,n);
+    for pi=1:length(ps)
+      p = ps(pi);
+      q = inhibit(p,n);
+      CPT{p} = CPT{p} .* [1 q];
+    end
+    % Arbitrarily attach the leak term to the first parent
+    p = ps(1);
+    q = leak(n);
+    CPT{p} = CPT{p} .* [q q];
+  end
+end
+
+for d=1:Ndiseases
+  bnet.CPD{d} = tabular_CPD(bnet, d, CPT{d}');
+end
+
+for i=1:Nfindings
+  fnode = finding_node(i);
+  fid = obs(i);
+  ps = parents(G, fid);
+  bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(fid), inhibit(ps, fid));
+end
+
+obs_nodes = finding_node;
+vals = sparse(1, maxNfindings);
+vals(pos) = 2;
+vals(neg) = 1;
+vals = full(vals(obs));
+
+
+
+
+