Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15450#discussion_r84247187
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala ---
@@ -323,7 +324,10 @@ class KMeans private (
* Initialize a set of cluster centers at random.
*/
private def initRandom(data: RDD[VectorWithNorm]): Array[VectorWithNorm]
= {
- data.takeSample(true, k, new
XORShiftRandom(this.seed).nextInt()).map(_.toDense)
+ // Select without replacement; may still produce duplicates if the
data has < k distinct
+ // points, so deduplicate the centroids to match the behavior of
k-means|| in the same situation
+ data.takeSample(false, k, new XORShiftRandom(this.seed).nextInt()).
+ map(_.vector).distinct.map(new VectorWithNorm(_))
--- End diff --
I'll move the dot. Agree, though computing k norms once seems negligible in
the context of the overall computation.
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