Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1628#discussion_r15611248
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---
@@ -453,4 +454,75 @@ class PythonMLLibAPI extends Serializable {
val ratings = ratingsBytesJRDD.rdd.map(unpackRating)
ALS.trainImplicit(ratings, rank, iterations, lambda, blocks, alpha)
}
+
+ /**
+ * Java stub for Python mllib RandomRDDGenerators.uniformRDD()
+ */
+ def uniformRDD(jsc: JavaSparkContext,
+ size: Long,
+ numPartitions: Int,
+ seed: Long): JavaRDD[Array[Byte]] = {
+ RandomRDDGenerators.uniformRDD(jsc.sc, size, numPartitions, seed)
+ .map(serializeDouble).toJavaRDD()
+ }
+
+ /**
+ * Java stub for Python mllib RandomRDDGenerators.normalRDD()
+ */
+ def normalRDD(jsc: JavaSparkContext,
+ size: Long,
+ numPartitions: Int,
+ seed: Long): JavaRDD[Array[Byte]] = {
+ RandomRDDGenerators.normalRDD(jsc.sc, size, numPartitions, seed)
+ .map(serializeDouble).toJavaRDD()
--- End diff --
You could actually leave out the `toJavaRDD()`s here and use the implicits
defined in `JavaRDD`; we do this in a lot of the other Java APIs, including
JavaRDD:
https://github.com/dorx/spark/blob/pythonRDD/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala#L88
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