#' @title Fuzzy C-Means Clustering Learner
#'
#' @name mlr_learners_clust.cmeans
#'
#' @description
#' A [LearnerClust] for fuzzy clustering implemented in [e1071::cmeans()].
#' [e1071::cmeans()] doesn't have a default value for the number of clusters.
#' Therefore, the `centers` parameter here is set to 2 by default.
#' The predict method uses [clue::cl_predict()] to compute the
#' cluster memberships for new data.
#'
#' @templateVar id clust.cmeans
#' @template learner
#'
#' @references
#' `r format_bib("dimitriadou2008misc", "bezdek2013pattern")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerClustCMeans = R6Class("LearnerClustCMeans",
inherit = LearnerClust,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
param_set = ps(
centers = p_uty(
tags = c("required", "train"), custom_check = check_centers
),
iter.max = p_int(1L, default = 100L, tags = "train"),
verbose = p_lgl(default = FALSE, tags = "train"),
dist = p_fct(levels = c("euclidean", "manhattan"), default = "euclidean", tags = "train"),
method = p_fct(levels = c("cmeans", "ufcl"), default = "cmeans", tags = "train"),
m = p_dbl(1, default = 2, tags = "train"),
rate.par = p_dbl(0, 1, tags = "train", depends = quote(method == "ufcl")),
weights = p_uty(default = 1L, tags = "train", custom_check = crate(function(x) {
if (test_numeric(x) && all(x > 0) || check_count(x, positive = TRUE)) {
TRUE
} else {
"`weights` must be positive numeric vector or a single positive number"
}
})),
control = p_uty(tags = "train")
)
param_set$set_values(centers = 2L)
super$initialize(
id = "clust.cmeans",
feature_types = c("logical", "integer", "numeric"),
predict_types = c("partition", "prob"),
param_set = param_set,
properties = c("partitional", "fuzzy", "complete"),
packages = "e1071",
man = "mlr3cluster::mlr_learners_clust.cmeans",
label = "Fuzzy C-Means Clustering Learner"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
assert_centers_param(pv$centers, task, test_data_frame, "centers")
m = invoke(e1071::cmeans, x = task$data(), .args = pv, .opts = allow_partial_matching)
if (self$save_assignments) {
self$assignments = m$cluster
}
m
},
.predict = function(task) {
partition = unclass(invoke(cl_predict, self$model, newdata = task$data(), type = "class_ids"))
prob = unclass(invoke(cl_predict, self$model, newdata = task$data(), type = "memberships"))
colnames(prob) = seq_len(ncol(prob))
PredictionClust$new(task = task, partition = partition, prob = prob)
}
)
)
#' @include zzz.R
register_learner("clust.cmeans", LearnerClustCMeans)
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