Github user ygcao commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r47429267
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -469,13 +495,13 @@ class Word2VecModel private[spark] (
this(Word2VecModel.buildWordIndex(model),
Word2VecModel.buildWordVectors(model))
}
- private def cosineSimilarity(v1: Array[Float], v2: Array[Float]): Double
= {
- require(v1.length == v2.length, "Vectors should have the same length")
- val n = v1.length
- val norm1 = blas.snrm2(n, v1, 1)
- val norm2 = blas.snrm2(n, v2, 1)
- if (norm1 == 0 || norm2 == 0) return 0.0
- blas.sdot(n, v1, 1, v2, 1) / norm1 / norm2
+ /**
+ * get the built vocabulary from the input
+ * this is useful for getting the whole vocabulary to join with other
data or filtering other data
+ * @return a map of word to its index
+ */
+ def getVocabulary: Map[String, Int] = {
--- End diff --
sure. Let's say, you want to use your built vectors for mapping WordNet
into vectors, both are big vocabulary but not have one-to-one relationship,
you'll have to join them to convert only trained words in wordnet into vectors.
doing the getVectors for each word in WordNet could cost you days while it can
be solved in minutes by using join.
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