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#' @title Divisive Hierarchical Clustering Learner
#'
#' @name mlr_learners_clust.diana
#' @include LearnerClust.R
#' @include aaa.R
#'
#' @description
#' A [LearnerClust] for divisive hierarchical clustering implemented in [cluster::diana()].
#' The predict method uses [stats::cutree()] which cuts the tree resulting from
#' hierarchical clustering into specified number of groups (see parameter `k`).
#' The default value for `k` is 2.
#'
#' @templateVar id clust.diana
#' @template learner
#' @template example
#'
#' @export
LearnerClustDiana = R6Class("LearnerClustDiana",
inherit = LearnerClust,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
metric = p_fct(default = "euclidean", levels = c("euclidean", "manhattan"), tags = "train"),
stand = p_lgl(default = FALSE, tags = "train"),
trace.lev = p_int(lower = 0L, default = 0L, tags = "train"),
k = p_int(lower = 1L, default = 2L, tags = "predict")
)
ps$values = list(k = 2L)
super$initialize(
id = "clust.diana",
feature_types = c("logical", "integer", "numeric"),
predict_types = "partition",
param_set = ps,
properties = c("hierarchical", "exclusive", "complete"),
packages = "cluster",
man = "mlr3cluster::mlr_learners_clust.diana",
label = "Divisive Hierarchical Clustering"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
m = invoke(cluster::diana, x = task$data(), diss = FALSE, .args = pv)
if (self$save_assignments) {
self$assignments = stats::cutree(m, self$param_set$values$k)
}
return(m)
},
.predict = function(task) {
if (test_true(self$param_set$values$k > task$nrow)) {
stop(sprintf("`k` needs to be between 1 and %s", task$nrow))
}
warn_prediction_useless(self$id)
PredictionClust$new(task = task, partition = self$assignments)
}
)
)
learners[["clust.diana"]] = LearnerClustDiana
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