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_kernel/Old/tabular_kernel.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_kernel/Old/tabular_kernel.m
new file mode 100644
index 00000000..99f74450
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_kernel/Old/tabular_kernel.m
@@ -0,0 +1,45 @@
+function K = tabular_kernel(fg, self)
+% TABULAR_KERNEL Make a table-based local kernel (discrete potential)
+% K = tabular_kernel(fg, self)
+%
+% fg is a factor graph
+% self is the number of a representative domain
+%
+% Use 'set_params_kernel' to adjust the following fields
+%   table - a q[1]xq[2]x... array, where q[i] is the number of values for i'th node
+%       in this domain [default: random values from [0,1], which need not sum to 1]
+
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  K = init_fields;
+  K = class(K, 'tabular_kernel');
+  return;
+elseif isa(fg, 'tabular_kernel')
+  % This might occur if we are copying an object.
+  K = fg;
+  return;
+end
+K = init_fields;
+
+ns = fg.node_sizes;
+dom = fg.doms{self};
+% we don't store the actual domain since it may vary due to parameter tieing
+K.sz = ns(dom);
+K.table = myrand(K.sz);
+
+K = class(K, 'tabular_kernel');
+
+
+%%%%%%%
+
+
+function K = 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.)
+
+K.table = [];
+K.sz = [];
+
+