\name{kernCompute} \Rdversion{1.0} \alias{kernCompute} \alias{kernDiagCompute} \alias{cmpndKernCompute} \alias{cmpndKernDiagCompute} \alias{disimKernCompute} \alias{disimKernDiagCompute} \alias{mlpKernCompute} \alias{multiKernCompute} \alias{multiKernDiagCompute} \alias{rbfKernCompute} \alias{rbfKernDiagCompute} \alias{simKernCompute} \alias{simKernDiagCompute} \alias{translateKernCompute} \alias{translateKernDiagCompute} \alias{whiteKernCompute} \alias{whiteKernDiagCompute} \alias{disimXdisimKernCompute} \alias{disimXrbfKernCompute} \alias{disimXsimKernCompute} \alias{simXrbfKernCompute} \alias{simXsimKernCompute} \alias{whiteXwhiteKernCompute} \title{Compute the kernel given the parameters and X.} \description{ Compute the kernel given the parameters and X. } \usage{ K <- kernCompute(kern, X) K <- kernCompute(kern, X1, X2) Kd <- kernDiagCompute(kern, X) } \arguments{ \item{kern}{kernel structure to be computed.} \item{X}{input data matrix (rows are data points) to the kernel computation.} \item{X1}{first input matrix to the kernel computation (forms the rows of the kernel).} \item{X2}{second input matrix to the kernel computation (forms the columns of the kernel).} } \details{ \code{K <- kernCompute(kern, X)} computes a kernel matrix for the given kernel type given an input data matrix. \code{K <- kernCompute(kern, X1, X2)} computes a kernel matrix for the given kernel type given two input data matrices, one for the rows and one for the columns. \code{K <- kernDiagCompute(kern, X)} computes the diagonal of a kernel matrix for the given kernel. \code{K <- *X*kernCompute(kern1, kern2, X)} \code{K <- *X*kernCompute(kern1, kern2, X1, X2)} same as above, but for cross combinations of two kernels, \code{kern1} and \code{kern2}. } \value{ \item{K}{computed elements of the kernel structure.} \item{Kd}{vector containing computed diagonal elements of the kernel structure.} } \seealso{ \code{\link{kernCreate}} } \examples{ kern <- kernCreate(1, 'rbf') K <- kernCompute(kern, as.matrix(3:8)) } \keyword{model}