Nick Pritchard created SPARK-10875:
--------------------------------------
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}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
