Github user ygcao commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r47435244
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -534,8 +577,15 @@ class Word2VecModel private[spark] (
// Need not divide with the norm of the given vector since it is
constant.
val cosVec = cosineVec.map(_.toDouble)
var ind = 0
+ var vecNorm = 1f
+ if (norm) {
--- End diff --
I don't mind much about make it always normalized. Just FYI: for current
brute-force kNN implementation in findSynonyms, the unnormalized version does
save potentially millions of division operation when the vocabulary is
millions, of course it is still in at most seconds saved. When caller only care
about top K without needs of metrics and call it for all their interested
words, the minor save of time could be multiplied again.
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