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Posted to issues@spark.apache.org by "Kyle Prifogle (JIRA)" <ji...@apache.org> on 2018/06/06 13:01:00 UTC
[jira] [Commented] (SPARK-15882) Discuss distributed linear algebra
in spark.ml package
[ https://issues.apache.org/jira/browse/SPARK-15882?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16503256#comment-16503256 ]
Kyle Prifogle commented on SPARK-15882:
---------------------------------------
Noticing this is almost 2 years old now which gives me the impression that this isn't going to be done? If I took the time to start to bite this off does anyone thing that they would use this or are most people finding their own more "expert" solutions :D ? Seems like matrix representations and basic algebra is pretty fundamental.
> Discuss distributed linear algebra in spark.ml package
> ------------------------------------------------------
>
> Key: SPARK-15882
> URL: https://issues.apache.org/jira/browse/SPARK-15882
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Reporter: Joseph K. Bradley
> Priority: Major
>
> This JIRA is for discussing how org.apache.spark.mllib.linalg.distributed.* should be migrated to org.apache.spark.ml.
> Initial questions:
> * Should we use Datasets or RDDs underneath?
> * If Datasets, are there missing features needed for the migration?
> * Do we want to redesign any aspects of the distributed matrices during this move?
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