#' @title BICO Clustering Learner
#'
#' @name mlr_learners_clust.bico
#'
#' @description
#' BICO (Fast computation of k-means coresets in a data stream) clustering.
#' Calls [stream::DSC_BICO()] from \CRANpkg{stream}.
#'
#' @templateVar id clust.bico
#' @template learner
#'
#' @references
#' `r format_bib("fichtenberger2013bico", "hahsler2017stream")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerClustBICO = R6Class("LearnerClustBICO",
inherit = LearnerClust,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
param_set = ps(
k = p_int(1L, default = 5L, tags = "train"),
space = p_int(1L, default = 10L, tags = "train"),
p = p_int(1L, default = 10L, tags = "train"),
iterations = p_int(1L, default = 10L, tags = "train")
)
super$initialize(
id = "clust.bico",
feature_types = c("integer", "numeric"),
predict_types = "partition",
param_set = param_set,
properties = c("partitional", "exclusive", "complete"),
packages = "stream",
man = "mlr3cluster::mlr_learners_clust.bico",
label = "BICO Clustering"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
dt = task$data()
m = invoke(stream::DSC_BICO, .args = pv)
x = stream::DSD_Memory(dt)
stats::update(m, x, n = nrow(dt))
if (self$save_assignments) {
self$assignments = as.integer(invoke(predict, m, newdata = dt)[[1L]])
}
m
},
.predict = function(task) {
partition = as.integer(invoke(predict, self$model, newdata = task$data())[[1L]])
PredictionClust$new(task = task, partition = partition)
}
)
)
#' @include zzz.R
register_learner("clust.bico", LearnerClustBICO)
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