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https://issues.apache.org/jira/browse/SPARK-5016?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14312284#comment-14312284
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Manoj Kumar commented on SPARK-5016:
------------------------------------
Well, I got mislead by the Jira description which says Gaussian Initialization.
I was thinking it was this block of code, that initializes the k Gaussian
distributions that needs to be parallelized.
{code}
val samples = breezeData.takeSample(withReplacement = true, k * nSamples, seed)
(Array.fill(k)(1.0 / k), Array.tabulate(k) { i =>
val slice = samples.view(i * nSamples, (i + 1) * nSamples)
new MultivariateGaussian(vectorMean(slice), initCovariance(slice))
})
{code}
And next time, please please don't post the code (or atleast give a spoiler
alert), it spoils the fun of fixing it :P
> GaussianMixtureEM should distribute matrix inverse for large numFeatures, k
> ---------------------------------------------------------------------------
>
> Key: SPARK-5016
> URL: https://issues.apache.org/jira/browse/SPARK-5016
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> If numFeatures or k are large, GMM EM should distribute the matrix inverse
> computation for Gaussian initialization.
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