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https://issues.apache.org/jira/browse/SPARK-10875?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-10875:
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Shepherd: Xiangrui Meng
Target Version/s: 1.6.0
> RowMatrix.computeCovariance() result is not exactly symmetric
> -------------------------------------------------------------
>
> Key: SPARK-10875
> URL: https://issues.apache.org/jira/browse/SPARK-10875
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.5.0
> Reporter: Nick Pritchard
> Assignee: Nick Pritchard
> Priority: Minor
>
> For some matrices, I have seen that the computed covariance matrix is not
> exactly symmetric, most likely due to some numerical rounding errors. This is
> problematic when trying to construct an instance of {{MultivariateGaussian}},
> because it requires an exactly symmetric covariance matrix. See reproducible
> example below.
> I would suggest modifying the implementation so that {{G(i, j)}} and {{G(j,
> i)}} are set at the same time, with the same value.
> {code}
> val rdd = RandomRDDs.normalVectorRDD(sc, 100, 10, 0, 0)
> val matrix = new RowMatrix(rdd)
> val mean = matrix.computeColumnSummaryStatistics().mean
> val cov = matrix.computeCovariance()
> val dist = new MultivariateGaussian(mean, cov) //throws
> breeze.linalg.MatrixNotSymmetricException
> {code}
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