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https://issues.apache.org/jira/browse/MAHOUT-682?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13031946#comment-13031946
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Vasil Vasilev commented on MAHOUT-682:
--------------------------------------
Jake,
While working on making the changes in MAHOUT-684 compliant with the changes in
this Jira I found the following issues in runIterationSequential:
1. Inference is created using:
if (inference == null) {
inference = new LDAInference(state);
}
however its state assignment is never updated (there is only update of the
state state = newState; at the end of the operation)
2. The log likelihood update (ll += doc.getLogLikelihood();) is not at the
correct place. It should be at the end of the outer for-loop (this iterating
over the training corpus)
> The LDA output does not include the topic-probability distribution per
> document (p(z|d)). It outputs only the topics and corresponding words.
> ---------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: MAHOUT-682
> URL: https://issues.apache.org/jira/browse/MAHOUT-682
> Project: Mahout
> Issue Type: Improvement
> Components: Clustering
> Affects Versions: 0.4
> Reporter: Himanshu Gahlot
> Assignee: Jake Mannix
> Fix For: 0.6
>
> Attachments: MAHOUT-458.patch, MAHOUT-458.patch
>
>
> The current implementation of LDA outputs only topics and their words. Many
> applications need the p(z|d) values of a document to use this vector as a
> reduced representation of the document (dimensionality reduction of
> document). We need to introduce a new key which would keep track of the gamma
> values for each document (as obtained from the document.infer() method) and
> writes these to the output stream and finally, PrintLDATopics should output
> these values per document id. Also, outputting the probabilities of words in
> a topic would also provide a more meaningful output.
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