net = gp(nin, covarfn) net = gp(nin, covarfn, prior)
net = gp(nin, covarfn) takes the number of inputs nin
for a Gaussian Process model with a single output, together
with a string covarfn which specifies the type of the covariance function,
and returns a data structure net. The parameters are set to zero.
The fields in net are
type = 'gp'
nin = number of inputs
nout = number of outputs: always 1
nwts = total number of weights and covariance function parameters
bias = logarithm of constant offset in covariance function
noise = logarithm of output noise variance
inweights = logarithm of inverse length scale for each input
covarfn = string describing the covariance function:
'sqexp'
'ratquad'
fpar = covariance function specific parameters (1 for squared exponential,
2 for rational quadratic)
trin = training input data (initially empty)
trtargets = training target data (initially empty)
net = gp(nin, covarfn, prior) sets a Gaussian prior on the
parameters of the model. prior must contain the fields
pr_mean and pr_variance. If pr_mean is a scalar,
then the Gaussian is assumed to be isotropic and the additional fields
net.pr_mean and pr_variance are set. Otherwise,
the Gaussian prior has a mean
defined by a column vector of parameters prior.pr_mean and
covariance defined by a column vector of parameters prior.pr_variance.
Each element of prmean corresponds to a separate group of parameters, which
need not be mutually exclusive. The membership of the groups is defined
by the matrix prior.index in which the columns correspond to the elements of
prmean. Each column has one element for each weight in the matrix,
in the order defined by the function gppak, and each element
is 1 or 0 according to whether the parameter is a member of the
corresponding group or not. The additional field net.index is set
in this case.
gppak, gpunpak, gpfwd, gperr, gpcovar, gpgradCopyright (c) Ian T Nabney (1996-9)