context("cluster_MiniBatchKmeans")
test_that("cluster_MiniBatchKmeans", {
requirePackagesOrSkip("ClusterR", default.method = "load")
parset.list = list(
list(),
list(clusters = 3L, batch_size = 5L)
)
old.predicts.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
if ("clusters" %in% names(parset)) {
clst = parset[["clusters"]]
} else {
clst = 2L
}
if ("batch_size" %in% names(parset)) {
btch.size = parset[["batch_size"]]
} else {
btch.size = 10L
}
m = ClusterR::MiniBatchKmeans(noclass.train,
clusters = clst, batch_size = btch.size)
p = as.integer(ClusterR::predict_MBatchKMeans(data = noclass.test,
CENTROIDS = m$centroids, fuzzy = FALSE))
old.predicts.list[[i]] = p
}
testSimpleParsets("cluster.MiniBatchKmeans", noclass.df,
character(0L), noclass.train.inds, old.predicts.list,
parset.list)
})
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