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#' @title K-Means Clustering Learner from Weka
#'
#' @name mlr_learners_clust.SimpleKMeans
#' @include LearnerClust.R
#' @include aaa.R
#'
#' @description
#' A [LearnerClust] for Simple K Means clustering implemented in [RWeka::SimpleKMeans()].
#' The predict method uses [RWeka::predict.Weka_clusterer()] to compute the
#' cluster memberships for new data.
#'
#' @templateVar id clust.SimpleKMeans
#' @template learner
#' @examples
#' \dontrun{
#' if (requireNamespace("RWeka")) {
#' learner = mlr3::lrn("clust.SimpleKMeans")
#' print(learner)
#'
#' # available parameters:
#' learner$param_set$ids()
#' }}
#'
#' @export
LearnerClustSimpleKMeans = R6Class("LearnerClustSimpleKMeans",
inherit = LearnerClust,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
A = p_uty(default = "weka.core.EuclideanDistance", tags = "train"),
C = p_lgl(default = FALSE, tags = "train"),
fast = p_lgl(default = FALSE, tags = "train"),
I = p_int(default = 100L, lower = 1L, tags = "train"),
init = p_int(default = 0L, lower = 0L, upper = 3L, tags = "train"),
M = p_lgl(default = FALSE, tags = "train"),
max_candidates = p_int(default = 100L, lower = 1L, tags = "train"),
min_density = p_int(default = 2L, lower = 1L, tags = "train"),
N = p_int(default = 2L, lower = 1L, tags = "train"),
num_slots = p_int(default = 1L, lower = 1L, tags = "train"),
O = p_lgl(default = FALSE, tags = "train"),
periodic_pruning = p_int(default = 10000L, lower = 1L, tags = "train"),
S = p_int(default = 10L, lower = 0L, tags = "train"),
t2 = p_dbl(default = -1, tags = "train"),
t1 = p_dbl(default = -1.5, tags = "train"),
V = p_lgl(default = FALSE, tags = "train"),
output_debug_info = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = "clust.SimpleKMeans",
feature_types = c("logical", "integer", "numeric"),
predict_types = "partition",
param_set = ps,
properties = c("partitional", "exclusive", "complete"),
packages = "RWeka",
man = "mlr3cluster::mlr_learners_clust.SimpleKMeans",
label = "K-Means (Weka)"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
names(pv) = chartr("_", "-", names(pv))
ctrl = do.call(RWeka::Weka_control, pv)
m = invoke(RWeka::SimpleKMeans, x = task$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)
}
)
)
learners[["clust.SimpleKMeans"]] = LearnerClustSimpleKMeans
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