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#' @title Farthest First Clustering Learner
#'
#' @name mlr_learners_clust.ff
#'
#' @description
#' A [LearnerClust] for Farthest First clustering implemented in [RWeka::FarthestFirst()].
#' The predict method uses [RWeka::predict.Weka_clusterer()] to compute the
#' cluster memberships for new data.
#'
#' @templateVar id clust.ff
#' @template learner
#'
#' @references
#' `r format_bib("witten2002data", "hochbaum1985best")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerClustFarthestFirst = R6Class("LearnerClustFF",
inherit = LearnerClust,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
param_set = ps(
N = p_int(1L, default = 2L, tags = "train"),
S = p_int(1L, default = 1L, tags = "train"),
output_debug_info = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = "clust.ff",
feature_types = c("logical", "integer", "numeric"),
predict_types = "partition",
param_set = param_set,
properties = c("partitional", "exclusive", "complete"),
packages = "RWeka",
man = "mlr3cluster::mlr_learners_clust.ff",
label = "Farthest First Clustering"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
names(pv) = chartr("_", "-", names(pv))
ctrl = do.call(RWeka::Weka_control, pv)
m = invoke(RWeka::FarthestFirst, x = task$data(), control = ctrl)
if (self$save_assignments) {
self$assignments = unname(m$class_ids + 1L)
}
return(m)
},
.predict = function(task) {
partition = predict(self$model, newdata = task$data(), type = "class") + 1L
PredictionClust$new(task = task, partition = partition)
}
)
)
#' @include aaa.R
learners[["clust.ff"]] = LearnerClustFarthestFirst
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