Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/5467#discussion_r28573221
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -479,9 +492,16 @@ class Word2VecModel private[mllib] (
*/
def findSynonyms(vector: Vector, num: Int): Array[(String, Double)] = {
require(num > 0, "Number of similar words should > 0")
- // TODO: optimize top-k
- val fVector = vector.toArray.map(_.toFloat)
- model.mapValues(vec => cosineSimilarity(fVector, vec))
+
+ val numWords = wordVectors.numRows
+ val cosineVec = Vectors.zeros(numWords).asInstanceOf[DenseVector]
+ BLAS.gemv(1.0, wordVectors, vector.asInstanceOf[DenseVector], 0.0,
cosineVec)
+
+ // Need not divide with the norm of the given vector since it is
constant.
+ val updatedCosines = indexedModel.map { case (_, ind) =>
--- End diff --
Do you mean that when I do a `dict.map`, the ordering need not be the same
as that of the dict?
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