This is ongoing work tracked by SPARK-4823 <https://issues.apache.org/jira/browse/SPARK-4823> with a PR for it here: PR6213 <https://github.com/apache/spark/pull/6213> - unfortunately the PR submitter didn't make it for Spark 1.5.
On Mon, Aug 31, 2015 at 4:17 AM, Maandy <dymc...@gmail.com> wrote: > I've been trying to implement cosine similarity using Spark and stumbled > upon > this article > > https://databricks.com/blog/2014/10/20/efficient-similarity-algorithm-now-in-spark-twitter.html > The only problem I have with it is that it seems that they assume that in > my > input file each *column* is a separate tweet, hence by computing column > similarity we will find similar tweets. Hope I understood it correctly? > > Since it's a Twitter based method I guess there has to be some logic behind > it but how would one go around preparing such a dataset? Personally after > doing TF-IDF on a document I get an RDD of Vectors which I can then > transform into a RowMatrix but this RowMatrix has each document as a > separate row not column hence the aforementioned method won't really help > me. I tried looking for some transpose methods but failed to find any. Am I > missing something or just misusing the available methods? > > Mateusz > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/MLlib-DIMSUM-row-similarity-tp24518.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >