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, 15 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/Old/update_params.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/Old/update_params.m
new file mode 100644
index 00000000..5a1e93a8
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/Old/update_params.m
@@ -0,0 +1,15 @@
+function CPD = update_params(CPD, ev, counts)
+% UPDATE_PARAMS Update the Dirichlet pseudo counts and compute the new MAP param estimates (tabular)
+%
+% CPD = update_params(CPD, ev) uses the evidence on the family from a single case.
+%
+% CPD = update_params(CPD, [], counts) does a batch update using the specified suff. stats.
+
+if nargin < 3
+  n = length(ev);
+  data = cat(1, ev{:}); % convert to a vector of scalars
+  counts = compute_counts(data(:)', 1:n, mysize(CPD.CPT));
+end
+  
+CPD.prior = CPD.prior + counts;
+CPD.CPT = mk_stochastic(CPD.prior);