net = gpinit(net, trin, trtargets, prior) net = gpinit(net, trin, trtargets, prior)
net = gpinit(net, trin, trtargets) takes a Gaussian Process data structure net
together
with a matrix trin of training input vectors and a matrix trtargets of
training target
vectors, and stores them in net. These datasets are required if
the corresponding inverse covariance matrix is not supplied to gpfwd.
This is important if the data structure is saved and then reloaded before
calling gpfwd.
Each row
of trin corresponds to one input vector and each row of trtargets
corresponds to one target vector.
net = gpinit(net, trin, trtargets, prior) additionally initialises the
parameters in net from the prior data structure which contains the
mean and variance of the Gaussian distribution which is sampled from.
x
and targets t:
net = gp(2, 'sqexp'); net = gpinit(net, x, t); % Train the network save 'gp.net' net;Another Matlab program can now read in the network and make predictions on a data set
testin:
load 'gp.net'; pred = gpfwd(net, testin);
gp, gpfwdCopyright (c) Ian T Nabney (1996-9)