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, 45 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_decision_node/tabular_decision_node.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_decision_node/tabular_decision_node.m
new file mode 100644
index 00000000..75ca5780
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_decision_node/tabular_decision_node.m
@@ -0,0 +1,45 @@
+function CPD = tabular_decision_node(bnet, self, CPT)
+% TABULAR_DECISION_NODE Represent a stochastic policy over a discrete decision/action node as a table
+% CPD = tabular_decision_node(bnet, self, CPT)
+%
+% node is the number of a node in this equivalence class.
+% CPT is an optional argument (see tabular_CPD for details); by default, it is the uniform policy.
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  CPD = class(CPD, 'tabular_decision_node', discrete_CPD(1, []));
+  return;
+elseif isa(bnet, 'tabular_decision_node')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+CPD = init_fields;
+
+ns = bnet.node_sizes;
+fam = family(bnet.dag, self);
+ps = parents(bnet.dag, self);
+sz = ns(fam);
+
+if nargin < 3
+  CPT = mk_stochastic(myones(sz)); 
+else
+  CPT = myreshape(CPT, sz);
+end
+
+CPD.CPT = CPT;
+CPD.sizes = sz; 
+
+clamped = 1; % don't update using EM
+CPD = class(CPD, 'tabular_decision_node', discrete_CPD(clamped, ns([ps self])));
+
+%%%%%%%%%%%
+
+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.CPT = [];
+CPD.sizes = [];