Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/16523#discussion_r95369332
--- Diff: R/pkg/R/mllib_clustering.R ---
@@ -204,11 +208,16 @@ setMethod("write.ml", signature(object =
"GaussianMixtureModel", path = "charact
#' @note spark.kmeans since 2.0.0
#' @seealso \link{predict}, \link{read.ml}, \link{write.ml}
setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula =
"formula"),
- function(data, formula, k = 2, maxIter = 20, initMode =
c("k-means||", "random")) {
+ function(data, formula, k = 2, maxIter = 20, initMode =
c("k-means||", "random"),
+ seed = NULL, initSteps = 2, tol = 1E-4) {
formula <- paste(deparse(formula), collapse = "")
initMode <- match.arg(initMode)
+ if (!is.null(seed)) {
+ seed <- as.character(as.integer(seed))
--- End diff --
I'd like to know why you convert ```seed``` to integer first and then
convert to character? AFAIK, the type of ```seed``` in MLlib is ```Long```
whose max value is ```9223372036854775807```. ```as.integer``` will return
```NA``` if it's beyond the scope of integer. Should we support consistent
range for ```seed``` across languages? It looks like R support for ```Long```
is not very well, if we only support integer, do we need to convert the integer
to character?
cc @felixcheung
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