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#' @export
makeRLearner.regr.laGP = function() {
makeRLearnerRegr(
cl = "regr.laGP",
package = "laGP",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "start", default = 6, lower = 6),
makeIntegerLearnerParam(id = "end", default = 50), # > start?
makeUntypedLearnerParam(id = "d", default = NULL),
makeUntypedLearnerParam(id = "g", default = 1 / 10000), # list?
makeDiscreteLearnerParam(id = "method", default = "alc",
values = c("alc", "alcray", "efi", "mspe", "nn")),
# FIXME: default values of "close" and "numrays" depend on the size of
# X, so the are not given here
makeIntegerLearnerParam(id = "close", lower = 0),
makeIntegerLearnerParam(id = "numrays", lower = 0,
requires = quote(method == "alcray")),
makeNumericLearnerParam(id = "verb", default = 0, tunable = FALSE)
),
properties = c("numerics", "se"),
name = "Local Approximate Gaussian Process",
short.name = "laGP",
callees = "aGP"
)
}
#' @export
trainLearner.regr.laGP = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
return(list(data = d$data, target = d$target, parset = list(...)))
}
#' @export
predictLearner.regr.laGP = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
pars = c(list(X = m$data, Z = m$target, XX = .newdata), Xi.ret = FALSE,
m$parset, list(...))
p = do.call(laGP::aGP, pars)
if (.learner$predict.type == "response") {
return(p$mean)
} else {
return(cbind(p$mean, sqrt(p$var)))
}
}
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