Not sure about the performance, but for now you could do:
val mat = new IndexedRowMatrix(...)
.toCoordinateMatrix()
.transpose()
.toRowMatrix()
On Mon, Aug 31, 2015 at 1:31 PM, Reza Zadeh <r...@databricks.com> wrote:
> 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.
>>
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>
