Evaluate gradient for kernel parameters (except bias) for the Sparse GP
covf = ogpcovgrad(net,xTrain);
g = ogpcovgrad(net) takes a Sparse OGP data structure net and
evaluates the gradient g of the negative log-likelihood with respect
to the hyperparameters of the model. The output g is a matrix with
columns of length #BV^2 (the kernel matrix is put into vector format). Each
column in g corresponds to a hyper-parameter and the order is provided
by ogppak.
g = ogpcovgrad(net,xTrain) additionally to the data structure
net, the procedure takes the elements of the training set xTrain
and returns on the columns the derivatives of the kernel function. The
result is thus a matrix with (nBV x nTr) lines and the number of
columns the number of hyperparameters.
If the kernel is specified by the user - i.e. kernel type is 'USER' -
then the last group of parameters is given by the user and there must exist
a field 'fngrad' in the structure 'net.kpar'. For details on
specifying a different kernel see the documentation for function ogpcovarf.
ogp, ogppak, ogpevidgrad, ogptrain
Copyright (c) Lehel Csató (2001-2004)