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#' @export
makeRLearner.cluster.FarthestFirst = function() {
makeRLearnerCluster(
cl = "cluster.FarthestFirst",
package = "RWeka",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "N", default = 2L, lower = 1L),
makeIntegerLearnerParam(id = "S", default = 1L, lower = 1L),
makeLogicalLearnerParam(id = "output-debug-info", default = FALSE, tunable = FALSE)
),
properties = "numerics",
name = "FarthestFirst Clustering Algorithm",
short.name = "farthestfirst",
callees = c("FarthestFirst", "Weka_control")
)
}
#' @export
trainLearner.cluster.FarthestFirst = function(.learner, .task, .subset, .weights = NULL, ...) {
ctrl = RWeka::Weka_control(...)
RWeka::FarthestFirst(getTaskData(.task, .subset), control = ctrl)
}
#' @export
predictLearner.cluster.FarthestFirst = function(.learner, .model, .newdata, ...) {
# RWeka returns cluster indices (i.e. starting from 0, which some tools don't like
as.integer(predict(.model$learner.model, .newdata, ...)) + 1L
}
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