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https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14073013#comment-14073013
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lukovnikov edited comment on SPARK-1405 at 7/24/14 9:10 AM:
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@Isaac, I think it's at
https://github.com/yinxusen/spark/blob/lda/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
and here (https://github.com/apache/spark/pull/476/files) for the other
changed files as well
was (Author: lukovnikov):
@Isaac, I think it's at
https://github.com/yinxusen/spark/blob/lda/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
> parallel Latent Dirichlet Allocation (LDA) atop of spark in MLlib
> -----------------------------------------------------------------
>
> Key: SPARK-1405
> URL: https://issues.apache.org/jira/browse/SPARK-1405
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.1.0
> Reporter: Xusen Yin
> Assignee: Xusen Yin
> Labels: features
> Fix For: 0.9.0
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> Latent Dirichlet Allocation (a.k.a. LDA) is a topic model which extracts
> topics from text corpus. Different with current machine learning algorithms
> in MLlib, instead of using optimization algorithms such as gradient desent,
> LDA uses expectation algorithms such as Gibbs sampling.
> In this PR, I prepare a LDA implementation based on Gibbs sampling, with a
> wholeTextFiles API (solved yet), a word segmentation (import from Lucene),
> and a Gibbs sampling core.
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