# kernel_exp
#' @title Exponential kernel
#'
#' @description Construct an exponential kernel.
#'
#' @details The exponential kernel takes the form:
#' \deqn{k_{expo}(\mathbf{x}, \mathbf{x}') = \sigma^2 exp\left(-\frac{1}{2} \sqrt{{\sum\limits_{d=1}^D \left(\frac{(x_d - x_d')}{2l_d^2}\right) ^ 2}}\right)}
#' where \eqn{\mathbf{x}} are the covariates on which the kernel is active, \eqn{l_d}
#' are the characteristic lengthscales for each covariate (column) \eqn{x_d}
#' and \eqn{\sigma^2} is the overall variance.
#'
#' Larger values of \eqn{l_i} correspond to functions in which change less
#' rapidly over the values of the covariates.
#'
#' @template par_sigma
#' @template par_l
#' @template kco
#' @template kco_basis
#' @export
#' @name expo
#'
#' @examples
#' # construct a kernel with one feature
#' k1 <- expo('temperature')
#'
#' # and another with two features
#' k2 <- expo(c('temperature', 'pressure'))
#'
#' # evaluate them on the pressure dataset
#' image(k1(pressure))
#' image(k2(pressure))
#'
expo <- function (columns, sigma = 1, l = rep(1, length(columns))) {
# construct an exponential kernel
createKernelConstructor('expo',
columns,
list(sigma = pos(sigma),
l = pos(l)),
expoEval)
}
expoEval <- function(object, data, newdata = NULL, diag = FALSE) {
# evaluate exponential kernel against data
# diagonal case
if (diag) {
# make sure it's symmetric (newdata is null)
checkSymmetric(newdata)
# if it's fine return sigma squared on the diagonals
covmat <- diagSigma(object, data)
return (covmat)
}
# extract from/to data
data <- getFeatures(object, data, newdata)
x <- data$x
y <- data$y
# get kernel parameters
parameters <- object$parameters
# extract lengthscales and variance
l <- parameters$l()
sigma <- parameters$sigma()
# apply the lengthscale parameters
x <- sweep(x, 2, l ^ 2, '/')
y <- sweep(y, 2, l ^ 2, '/')
# get distances
d <- fields::rdist(x, y)
# complete covariance matrix
covmat <- sigma ^ 2 * exp(-(d) / 2)
# and return
return (covmat)
}
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