[jira] [Created] (SPARK-10875) RowMatrix.computeCovariance() result is not exactly symmetric

["Nick Pritchard (JIRA)" <ji...@apache.org>]

Nick Pritchard created SPARK-10875:
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             Summary: 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


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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