https://github.com/berndbischl/mlr
Tip revision: c27261d12b9c0e277d044d54f00127500109b734 authored by Bernd Bischl on 29 October 2014, 01:15:38 UTC
clean up
clean up
Tip revision: c27261d
RLearner_regr_fnn.R
#' @export
makeRLearner.regr.fnn = function() {
makeRLearnerRegr(
cl = "regr.fnn",
package = "FNN",
# l is for reject option. cannot be done with mlr atm
par.set = makeParamSet(
makeIntegerLearnerParam(id = "k", default = 1L, lower = 1L),
makeLogicalLearnerParam(id = "use.all", default = TRUE, requires = expression(algorithm == "VR")),
makeDiscreteLearnerParam(id = "algorithm", default = "cover_tree", values = list("cover_tree", "kd_tree", "VR"))
),
properties = c("numerics"),
name = "Fast k-Nearest Neighbor",
short.name = "fnn",
note = ""
)
}
#' @export
trainLearner.regr.fnn = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
list(train = d, parset = list(...))
}
#' @export
predictLearner.regr.fnn = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
pars = c(list(train = m$train$data, test = .newdata, y = m$train$target), m$parset, list(...))
do.call(FNN::knn.reg, pars)$pred
}