#' @title Gaussian Mixture Models-Based Clustering Learner
#'
#' @name mlr_learners_clust.mclust
#'
#' @description
#' A [LearnerClust] for model-based clustering implemented in [mclust::Mclust()].
#' The predict method uses [mclust::predict.Mclust()] to compute the
#' cluster memberships for new data.
#'
#' @templateVar id clust.mclust
#' @template learner
#'
#' @references
#' `r format_bib("scrucca2016mclust", "fraley2002model")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerClustMclust = R6Class("LearnerClustMclust",
inherit = LearnerClust,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
parma_set = ps(
G = p_uty(default = 1:9, tags = "train", custom_check = check_numeric),
modelNames = p_uty(tags = "train", custom_check = check_character),
prior = p_uty(tags = "train", custom_check = check_list),
control = p_uty(tags = "train", custom_check = check_list),
initialization = p_uty(tags = "train", custom_check = check_list),
x = p_uty(tags = "train", custom_check = crate(function(x) check_class(x, "mclustBIC")))
)
super$initialize(
id = "clust.mclust",
feature_types = c("logical", "integer", "numeric"),
predict_types = c("partition", "prob"),
param_set = parma_set,
properties = c("partitional", "fuzzy", "complete"),
packages = "mclust",
man = "mlr3cluster::mlr_learners_clust.mclust",
label = "Gaussian Mixture Models Clustering"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
with_package("mclust", {
m = invoke(mclust::Mclust, data = task$data(), .args = pv)
})
if (self$save_assignments) {
self$assignments = m$classification
}
m
},
.predict = function(task) {
predictions = invoke(predict, self$model, newdata = task$data())
partition = as.integer(predictions$classification)
prob = predictions$z
PredictionClust$new(task = task, partition = partition, prob = prob)
}
)
)
#' @include zzz.R
register_learner("clust.mclust", LearnerClustMclust)
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