The similarities returned are not in fact true cosine similarities as they
are not properly normalized - this will be fixed in this PR:
https://github.com/apache/spark/pull/10152
On Tue, Dec 15, 2015 at 2:54 AM, jxieeducation <jxieeducat...@gmail.com>
wrote:
> Hi,
>
> For Word2Vec in Mllib, when I use a large number of partitions (e.g. 256),
> my vectors turn out to be very large. I am looking for a representation
> that
> is between (-1, 1) like all other Word2Vec implementations (e.g. Gensim,
> google's Word2Vec).
>
> E.g.
>
> scala> var m = model.transform("SOMETHING")
>
> m: org.apache.spark.mllib.linalg.Vector =
>
> [1.61478590965271,-13.385428428649902,-19.518991470336914,12.05420970916748,-6.141440391540527...]
>
>
>
> Thanks so much!
>
>
>
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