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, 48 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@root_CPD/root_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@root_CPD/root_CPD.m
new file mode 100644
index 00000000..b07df1e5
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@root_CPD/root_CPD.m
@@ -0,0 +1,48 @@
+function CPD = root_CPD(bnet, self, val)
+% ROOT_CPD Make a conditional prob. distrib. which has no parameters.
+% CPD = ROOT_CPD(BNET, NODE_NUM, VAL)
+%
+% The node must not have any parents and is assumed to always be observed.
+% It is a way of modelling exogenous inputs to a model.
+% VAL is the value to which the root is clamped (default: [])
+
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  CPD = class(CPD, 'root_CPD', generic_CPD(1));
+  return;
+elseif isa(bnet, 'root_CPD')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+CPD = init_fields;
+
+
+if nargin < 3, val = []; end
+
+ns = bnet.node_sizes;
+ps = parents(bnet.dag, self);
+if ~isempty(ps)
+  error('root CPDs should have no parents')
+end
+
+CPD.self = self;
+CPD.val = val;
+CPD.sizes = ns(self);
+
+clamped = 1;
+CPD = class(CPD, 'root_CPD', generic_CPD(clamped));
+
+
+%%%%%%%%%%%
+
+function CPD = init_fields()
+% This ensures we define the fields in the same order 
+% no matter whether we load an object from a file,
+% or create it from scratch. (Matlab requires this.)
+
+CPD.self = [];
+CPD.val = [];
+CPD.sizes = [];