diff toolboxes/FullBNT-1.0.7/netlab3.3/gpfwd.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpfwd.m	Tue Feb 10 15:05:51 2015 +0000
@@ -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