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https://issues.apache.org/jira/browse/MAHOUT-524?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13133524#comment-13133524
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Shannon Quinn commented on MAHOUT-524:
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If there are two DLS.runJob() methods and the spectral code is the only bit of
code that calls one of the two runJob() methods, then in the interest of making
the codebase just a tiny bit more maintainable I would vote for switching out
the runJob() invoked by the spectral code and deleting the other one in DLS
entirely.
Regarding your tracing of the DRM.times() method, I was having the same
problem: the fact that there exist so many chained job constructors makes it
difficult to follow. Is there any way we could simplify TimesSquaredJob? Are
each of those job creation methods called multiple times throughout the code
base?
Regarding this issue, it sounds like the problem either resides in TimesSquared
not correctly setting the path as you mentioned (but this begs the question why
no other algorithm which uses DRM.times() is running into the same problem), or
the Configuration voodoo in SKMD is causing problems.
I'll investigate the manipulation of Configuration objects in SKMD this week.
If you have any thoughts on the other points, please let me know.
> DisplaySpectralKMeans example fails
> -----------------------------------
>
> Key: MAHOUT-524
> URL: https://issues.apache.org/jira/browse/MAHOUT-524
> Project: Mahout
> Issue Type: Bug
> Components: Clustering
> Affects Versions: 0.4, 0.5
> Reporter: Jeff Eastman
> Assignee: Shannon Quinn
> Labels: clustering, k-means, visualization
> Fix For: 0.6
>
> Attachments: EclipseLog_20110918.txt,
> SpectralKMeans_fail_20110919.txt, aff.txt, raw.txt, spectralkmeans.png
>
>
> I've committed a new display example that attempts to push the standard
> mixture of models data set through spectral k-means. After some tweaking of
> configuration arguments and a bug fix in EigenCleanupJob it runs spectral
> k-means to completion. The display example is expecting 2-d clustered points
> and the example is producing 5-d points. Additional I/O work is needed before
> this will play with the rest of the clustering algorithms.
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