[jira] [Commented] (SPARK-9245) DistributedLDAModel predict top topic per doc-term instance

["Meihua Wu (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-9245?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14651319#comment-14651319 ] 

Meihua Wu commented on SPARK-9245:
----------------------------------

[~josephkb]: would like to confirm: (using notation Asuncion 2009), for doc `j` and term `w`, find the topic `k` such that gamma_wjk is maximized?



> DistributedLDAModel predict top topic per doc-term instance
> -----------------------------------------------------------
>
>                 Key: SPARK-9245
>                 URL: https://issues.apache.org/jira/browse/SPARK-9245
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Joseph K. Bradley
>   Original Estimate: 48h
>  Remaining Estimate: 48h
>
> For each (document, term) pair, return top topic.  Note that instances of (doc, term) pairs within a document (a.k.a. "tokens") are exchangeable, so we should provide an estimate per document-term, rather than per token.
> Synopsis for DistributedLDAModel:
> {code}
> /** @return RDD of (doc ID, vector of top topic index for each term) */
> def topTopicAssignments: RDD[(Long, Vector)]
> {code}
> Note that using Vector will let us have a sparse encoding which is Java-friendly.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org