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, 68 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/mk_water_dbn.m b/sourcecodes/bnt-master/BNT/examples/dynamic/mk_water_dbn.m
new file mode 100644
index 00000000..38cc5124
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/mk_water_dbn.m
@@ -0,0 +1,68 @@
+function bnet = mk_water_dbn(discrete_obs, obs_leaves)
+% MK_WATER_DBN
+% bnet = mk_water_dbn(discrete_obs, obs_leaves)
+%
+% If discrete_obs = 1 (default), the leaves are binary, else scalar Gaussians
+% If obs_leaves = 1, all the leaves are observed, otherwise rnd nodes are observed
+%
+% This is a model of the biological processes of a water purification plant, developed
+% by Finn V. Jensen, Uffe Kjærulff, Kristian G. Olesen, and Jan Pedersen.
+% See http://www-nt.cs.berkeley.edu/home/nir/public_html/Repository/water.htm
+% See also Boyen and Koller, "Tractable Inference for Complex Stochastic Processes", UAI98
+
+if nargin < 1, discrete_obs = 1; end
+if nargin < 1, obs_leaves = 1; end
+
+ss = 12;
+intra = zeros(ss);
+intra(1,9) = 1;
+intra(3,10) = 1;
+intra(4,11) = 1;
+intra(8,12) = 1;
+
+inter = zeros(ss);
+inter(1, [1 3]) = 1;
+inter(2, [2 3 7]) = 1;
+inter(3, [3 4 5]) = 1;
+inter(4, [3 4 6]) = 1;
+inter(5, [3 5 6]) = 1;
+inter(6, [4 5 6]) = 1;
+inter(7, [7 8]) = 1;
+inter(8, [6 7 8]) = 1;
+
+if obs_leaves
+  onodes = 9:12; % leaves
+else
+  onodes = [1 5 9:12]; % throw in some other nodes
+end
+hnodes = 1:8;
+if discrete_obs
+  ns = 2*ones(1 ,ss);
+  dnodes = 1:ss;
+else
+  ns = [2*ones(1,length(hnodes)) 1*ones(length(onodes))];
+  dnodes = hnodes;
+end
+
+eclass1 = 1:12;
+eclass2 = [13:20 9:12];
+bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
+	      'observed', onodes);
+if discrete_obs
+  for i=1:max(eclass2)
+    bnet.CPD{i} = tabular_CPD(bnet, i);
+  end
+else
+  for i=hnodes(:)'
+    bnet.CPD{i} = tabular_CPD(bnet, i);
+  end
+  for i=onodes(:)'
+    bnet.CPD{i} = gaussian_CPD(bnet, i);
+  end
+  for i=hnodes(:)'+ss
+    bnet.CPD{i} = tabular_CPD(bnet, i);
+  end
+end
+
+
+