From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: 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
---
 .../BNT/examples/static/Models/mk_ideker_bnet.m    | 52 ++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 sourcecodes/bnt-master/BNT/examples/static/Models/mk_ideker_bnet.m

(limited to 'sourcecodes/bnt-master/BNT/examples/static/Models/mk_ideker_bnet.m')

diff --git a/sourcecodes/bnt-master/BNT/examples/static/Models/mk_ideker_bnet.m b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_ideker_bnet.m
new file mode 100644
index 00000000..67f95bae
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_ideker_bnet.m
@@ -0,0 +1,52 @@
+function bnet = mk_ideker_bnet(CPD_type, p)
+% MK_IDEKER_BNET Make the Bayes net in the PSB'00 paper by Ideker, Thorsson and Karp.
+%
+% BNET = MK_IDEKER_BNET uses the boolean functions specified in the paper 
+% "Discovery of regulatory interactions through perturbation: inference and experimental design",
+% Pacific Symp. on Biocomputing, 2000.
+% 
+% BNET = MK_IDEKER_BNET('root') uses the above boolean functions, but puts a uniform
+% distribution on the root nodes.
+%
+% BNET = MK_IDEKER_BNET('cpt', p) uses random parameters drawn from a Dirichlet(p,p,...)
+% distribution. If p << 1, this is nearly deterministic; if p >> 1, this is nearly uniform.
+% 
+% BNET = MK_IDEKER_BNET('bool') makes each CPT a random boolean function.
+%
+% BNET = MK_IDEKER_BNET('orig') is the same as MK_IDEKER_BNET.
+
+
+if nargin == 0
+  CPD_type = 'orig';
+end
+
+n = 4;
+dag = zeros(n);
+dag(1,3)=1;
+dag(2,[3 4])=1;
+dag(3,4)=1;
+ns = 2*ones(1,n);
+bnet = mk_bnet(dag, ns);
+
+switch CPD_type
+ case 'orig',
+  bnet.CPD{1} = tabular_CPD(bnet, 1, [0 1]);
+  bnet.CPD{2} = tabular_CPD(bnet, 2, [0 1]);
+  bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
+  bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
+ case 'root',
+  bnet.CPD{1} = tabular_CPD(bnet, 1, [0.5 0.5]);
+  bnet.CPD{2} = tabular_CPD(bnet, 2, [0.5 0.5]);
+  bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
+  bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
+ case 'bool',
+  for i=1:n
+    bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd');
+  end
+ case 'cpt',
+  for i=1:n
+    bnet.CPD{i} = tabular_CPD(bnet, i, p);
+  end
+ otherwise,
+  error(['unknown type ' CPD_type]);
+end
-- 
cgit 1.4.1