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, 37 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/HMM/mhmmParzen_train_observed.m b/sourcecodes/bnt-master/HMM/mhmmParzen_train_observed.m
new file mode 100644
index 00000000..1786e9a2
--- /dev/null
+++ b/sourcecodes/bnt-master/HMM/mhmmParzen_train_observed.m
@@ -0,0 +1,37 @@
+function [initState, transmat, mu, Nproto, pick] = mhmmParzen_train_observed(obsData, hiddenData, ...
+						  nstates, maxNproto, varargin)
+% mhmmParzentrain_observed  with mixture of Gaussian outputs from fully observed sequences
+% function [initState, transmat, mu, Nproto] = mhmm_train_observed_parzen(obsData, hiddenData, ...
+%						  nstates, maxNproto)
+%
+%
+% INPUT
+% If all sequences have the same length
+% obsData(:,t,ex) 
+% hiddenData(ex,t)  - must be ROW vector if only one sequence
+% If sequences have different lengths, we use cell arrays
+% obsData{ex}(:,t) 
+% hiddenData{ex}(t)
+%
+% Optional argumnets
+% dirichletPriorWeight - for smoothing transition matrix counts
+% mkSymmetric
+%
+% Output
+% mu(:,q)
+% Nproto(q) is the number of prototypes (mixture components) chosen for state q
+
+[transmat, initState] = transmat_train_observed(...
+    hiddenData, nstates, varargin{:});
+
+% convert to obsData(:,t*nex)
+if ~iscell(obsData)
+  [D T Nex] = size(obsData);
+  obsData = reshape(obsData, D, T*Nex);
+else
+  obsData = cat(2, obsData{:});
+  hiddenData = cat(2, hiddenData{:});
+end
+[mu, Nproto, pick] = parzen_fit_select_unif(obsData, hiddenData(:), maxNproto);
+
+