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#' @export
makeRLearner.classif.rknn = function() {
makeRLearnerClassif(
cl = "classif.rknn",
package = "rknn",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "k", default = 1L, lower = 1L, upper = 98L),
makeIntegerLearnerParam(id = "r", default = 500L, lower = 1L),
makeIntegerLearnerParam(id = "mtry", lower = 1L),
makeIntegerLearnerParam(id = "seed", lower = 1L),
makeUntypedLearnerParam(id = "cluster", default = NULL)
),
# rknn can't handle unordered factors or return probs
properties = c("twoclass", "multiclass", "numerics", "ordered"),
name = "Random k-Nearest-Neighbors",
short.name = "rknn",
note = "k restricted to < 99 as the code allocates arrays of static size",
callees = "rknn"
)
}
#' @export
trainLearner.classif.rknn = function(.learner, .task, .subset, .weights = NULL, ...) {
z = getTaskData(.task, .subset, target.extra = TRUE)
c(list(data = z$data, y = z$target), list(...))
}
#' @export
predictLearner.classif.rknn = function(.learner, .model, .newdata, ...) {
args = .model$learner.model
args$newdata = .newdata
do.call(rknn::rknn, args)$pred
}
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