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# October 26, 2018
#' Class \code{CVInfokParam}
#'
#' Class \code{CVInfokParam} holds information regarding cross-validation
#' procedure when only multiple kernel parameters values are considered.
#'
#' @name CVInfokParam-class
#'
#' @slot value Array of values at parameters considered
#'
#' @include N_CVInfo.R N_CVBasic.R N_CVInfoLambda.R
#'
#' @keywords internal
setClass(Class = "CVInfokParam",
slots = c(value = "array"),
contains = c("CVInfo"))
##########
## METHODS
##########
#' Methods Available for Objects of Class \code{CVInfokParam}
#'
#' @name CVInfokParam-methods
#'
#' @keywords internal
NULL
#' @rdname newCVInfo
setMethod(f = ".newCVInfo",
signature = c(lambdas = "numeric",
kernel = "MultiRadialKernel"),
definition = function(lambdas,
kernel,
methodObject,
cvObject,
suppress, ...) {
nk <- length(x = kernel@kparam)
valueKparam <- array(data = rep(x = 0.0, times = nk),
dimnames = list(round(x = kernel@kparam,
digits = 3L)))
kernels <- methodObject@kernel
for (j in 1L:nk) {
if (suppress != 0L) {
cat("Cross-validation for kparam =", kernel@kparam[j], "\n")
}
# replace kernel stored in methodsObject with new one
methodObject@kernel <- new("RadialKernel",
model = kernel@model,
kparam = kernel@kparam[j])
methodObject@kernel@X <- kernel@X
# complete cross-validation step
res <- .newCVStep(cvObject = cvObject,
methodObject = methodObject,
lambda = lambdas[1L],
suppress = suppress, ...)
# .newCVStep return NULL if training not successful
if (is.null(x = res)) {
valueKparam[j] <- NA
} else {
valueKparam[j] <- res
}
}
# if no training was successful return NA
if (all(is.na(x = valueKparam)) ) return( NA )
# accept the parameter value yielding the largest value as optimal
ivl <- which.max(x = valueKparam)
kparam <- kernel@kparam[ivl]
if (suppress != 0L) {
cat("Selected parameter: kparam =", kparam, "\n")
}
optKernel <- as(object = kernel, Class = "Kernel")
optKernel@kparam <- kparam
optKernel <- as(object = optKernel, Class = "RadialKernel")
result <- new(Class = "CVInfokParam",
"value" = valueKparam,
"params" = list("lambda" = lambdas,
"kparam" = kernel@kparam),
"optimal" = list("lambda" = lambdas,
"kernel" = optKernel))
return( result )
})
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