wolffd@0: wolffd@0:
wolffd@0:wolffd@0: edata = gperr(net, x, t) wolffd@0: [e, edata, eprior] = gperr(net, x, t) wolffd@0:wolffd@0: wolffd@0: wolffd@0:
e = gperr(net, x, t)
takes a Gaussian Process data structure net
together
wolffd@0: with a matrix x
of input vectors and a matrix t
of target
wolffd@0: vectors, and evaluates the error function e
. Each row
wolffd@0: of x
corresponds to one input vector and each row of t
wolffd@0: corresponds to one target vector.
wolffd@0:
wolffd@0: [e, edata, eprior] = gperr(net, x, t)
additionally returns the
wolffd@0: data and hyperprior components of the error, assuming a Gaussian
wolffd@0: prior on the weights with mean and variance parameters prmean
and
wolffd@0: prvariance
taken from the network data structure net
.
wolffd@0:
wolffd@0:
gp
, gpcovar
, gpfwd
, gpgrad
Copyright (c) Ian T Nabney (1996-9) wolffd@0: wolffd@0: wolffd@0: wolffd@0: