#' @export
makeRLearner.cluster.MiniBatchKmeans = function() {
makeRLearnerCluster(
cl = "cluster.MiniBatchKmeans",
package = "ClusterR",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "clusters", default = 2L, lower = 1L),
makeIntegerLearnerParam(id = "batch_size", default = 10L, lower = 1L),
makeIntegerLearnerParam(id = "num_init", default = 1L, lower = 1L),
makeIntegerLearnerParam(id = "max_iters", default = 100L, lower = 1L),
makeNumericLearnerParam(id = "init_fraction", default = 1, lower = 0),
makeDiscreteLearnerParam(id = "initializer", default = "kmeans++",
values = c("optimal_init", "quantile_init", "kmeans++", "random")),
makeIntegerLearnerParam(id = "early_stop_iter", default = 10L, lower = 1L),
makeLogicalLearnerParam(id = "verbose", default = FALSE,
tunable = FALSE),
makeUntypedLearnerParam(id = "CENTROIDS", default = NULL),
makeNumericLearnerParam(id = "tol", default = 1e-04, lower = 0),
makeNumericLearnerParam(id = "tol_optimal_init", default = 0.3, lower = 0),
makeIntegerLearnerParam(id = "seed", default = 1L)
),
par.vals = list(clusters = 2L),
properties = c("numerics", "prob"),
name = "MiniBatchKmeans",
note = "Calls MiniBatchKmeans of package ClusterR. Argument `clusters` has default value of 2 if not provided by user.",
short.name = "MBatchKmeans",
callees = c("MiniBatchKmeans", "predict_MBatchKMeans")
)
}
#' @export
trainLearner.cluster.MiniBatchKmeans = function(.learner, .task, .subset, .weights = NULL, ...) {
ClusterR::MiniBatchKmeans(getTaskData(.task, .subset), ...)
}
#' @export
predictLearner.cluster.MiniBatchKmeans = function(.learner, .model, .newdata, ...) {
if (.learner$predict.type == "prob") {
pred = ClusterR::predict_MBatchKMeans(data = .newdata,
CENTROIDS = .model$learner.model$centroids,
fuzzy = TRUE, ...)
res = pred$fuzzy_clusters
return(res)
} else {
pred = ClusterR::predict_MBatchKMeans(data = .newdata,
CENTROIDS = .model$learner.model$centroids,
fuzzy = FALSE, ...)
res = as.integer(pred)
return(res)
}
}
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