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, 51 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gpfwd.m b/sourcecodes/bnt-master/netlab3.3/gpfwd.m
new file mode 100644
index 00000000..b2d390e5
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/gpfwd.m
@@ -0,0 +1,51 @@
+function [y, sigsq] = gpfwd(net, x, cninv)
+%GPFWD	Forward propagation through Gaussian Process.
+%
+%	Description
+%	Y = GPFWD(NET, X) takes a Gaussian Process data structure NET
+%	together  with a matrix X of input vectors, and forward propagates
+%	the inputs through the model to generate a matrix Y of output
+%	vectors.  Each row of X corresponds to one input vector and each row
+%	of Y corresponds to one output vector.  This assumes that the
+%	training data (both inputs and targets) has been stored in NET by a
+%	call to GPINIT; these are needed to compute the training data
+%	covariance matrix.
+%
+%	[Y, SIGSQ] = GPFWD(NET, X) also generates a column vector SIGSQ of
+%	conditional variances (or squared error bars) where each value
+%	corresponds to a pattern.
+%
+%	[Y, SIGSQ] = GPFWD(NET, X, CNINV) uses the pre-computed inverse
+%	covariance matrix CNINV in the forward propagation.  This increases
+%	efficiency if several calls to GPFWD are made.
+%
+%	See also
+%	GP, DEMGP, GPINIT
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(net, 'gp', x);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+if ~(isfield(net, 'tr_in') & isfield(net, 'tr_targets'))
+   error('Require training inputs and targets');
+end
+
+if nargin == 2
+  % Inverse covariance matrix not supplied.
+  cninv = inv(gpcovar(net, net.tr_in));
+end
+ktest = gpcovarp(net, x, net.tr_in);
+
+% Predict mean
+y = ktest*cninv*net.tr_targets;
+
+if nargout >= 2
+  % Predict error bar
+  ndata = size(x, 1);
+  sigsq = (ones(ndata, 1) * gpcovarp(net, x(1,:), x(1,:))) ...
+    - sum((ktest*cninv).*ktest, 2); 
+end