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, 76 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/Models/mk_asia_bnet.m b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_asia_bnet.m
new file mode 100644
index 00000000..fce24c3a
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Models/mk_asia_bnet.m
@@ -0,0 +1,76 @@
+function bnet = mk_asia_bnet(CPD_type, p, arity)
+% MK_ASIA_BNET Make the 'Asia' bayes net.
+%
+% BNET = MK_ASIA_BNET uses the parameters specified on p21 of Cowell et al, 
+% "Probabilistic networks and expert systems", Springer Verlag 1999.
+% 
+% BNET = MK_ASIA_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_ASIA_BNET('bool') makes each CPT a random boolean function.
+%
+% BNET = MK_ASIA_BNET('gauss') makes each CPT a random linear Gaussian distribution.
+%
+% BNET = MK_ASIA_BNET('orig') is the same as MK_ASIA_BNET.
+%
+% BNET = MK_ASIA_BNET('cpt', p, arity) can specify non-binary nodes.
+
+
+if nargin == 0, CPD_type = 'orig'; end
+if nargin < 3, arity = 2; end
+
+Smoking = 1;
+Bronchitis = 2;
+LungCancer = 3;
+VisitToAsia = 4;
+TB = 5;
+TBorCancer = 6;
+Dys = 7;
+Xray = 8;
+
+n = 8;
+dag = zeros(n);
+dag(Smoking, [Bronchitis LungCancer]) = 1;
+dag(Bronchitis, Dys) = 1;
+dag(LungCancer, TBorCancer) = 1;
+dag(VisitToAsia, TB) = 1;
+dag(TB, TBorCancer) = 1;
+dag(TBorCancer, [Dys Xray]) = 1;
+
+ns = arity*ones(1,n);
+if strcmp(CPD_type, 'gauss')
+  dnodes = [];
+else
+  dnodes = 1:n;
+end
+bnet = mk_bnet(dag, ns, 'discrete', dnodes);
+
+switch CPD_type
+  case 'orig', 
+    % true is 2, false is 1
+    bnet.CPD{VisitToAsia} = tabular_CPD(bnet, VisitToAsia, [0.99   0.01]);
+    bnet.CPD{Bronchitis} = tabular_CPD(bnet, Bronchitis, [0.7 0.4   0.3 0.6]);
+    % minka: bug fix
+    bnet.CPD{Dys} = tabular_CPD(bnet, Dys, [0.9 0.2 0.3 0.1   0.1 0.8 0.7 0.9]);
+    bnet.CPD{TBorCancer} = tabular_CPD(bnet, TBorCancer, [1 0 0 0   0 1 1 1]);
+    % minka: bug fix
+    bnet.CPD{LungCancer} = tabular_CPD(bnet, LungCancer, [0.99 0.9  0.01 0.1]);
+    bnet.CPD{Smoking} = tabular_CPD(bnet, Smoking, [0.5 0.5]);
+    bnet.CPD{TB} = tabular_CPD(bnet, TB, [0.99 0.95  0.01 0.05]);
+    bnet.CPD{Xray} = tabular_CPD(bnet, Xray, [0.95 0.02  0.05 0.98]);
+ case 'bool',
+  for i=1:n
+    bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd');
+  end
+ case 'gauss',
+  for i=1:n
+    bnet.CPD{i} = gaussian_CPD(bnet, i, 'cov', 1*eye(ns(i)));
+  end
+ case 'cpt',
+  for i=1:n
+    bnet.CPD{i} = tabular_CPD(bnet, i, p);
+  end
+end
+
+  
+