Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11812#discussion_r56619995
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -532,28 +539,14 @@ class Word2VecModel private[spark] (
blas.sgemv(
"T", vectorSize, numWords, alpha, wordVectors, vectorSize, fVector,
1, beta, cosineVec, 1)
- // Need not divide with the norm of the given vector since it is
constant.
val cosVec = cosineVec.map(_.toDouble)
- var ind = 0
- val vecNorm = blas.snrm2(vectorSize, fVector, 1)
--- End diff --
Removing this means that passing in a vector that is not in the vocab will
not give the expected result as it is not explicitly normalized. As an example,
some applications may pass in the average vector for a set of words to compute
synonyms.
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