Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/22784#discussion_r228714200
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -384,18 +384,28 @@ class RowMatrix @Since("1.0.0") (
val n = numCols().toInt
require(k > 0 && k <= n, s"k = $k out of range (0, n = $n]")
- val Cov = computeCovariance().asBreeze.asInstanceOf[BDM[Double]]
+ if (n > 65535) {
+ val svd = computeSVD(k)
+ val s = svd.s.toArray.map(eigValue => eigValue * eigValue / (n - 1))
--- End diff --
My linear algebra is probably rusty, so check me here, but we need the
eigendecomposition of the covariance matrix 1/(n-1) * mat' * mat. I get how the
singular values here give these eigenvalues. Don't the singular vectors V need
to be divided by n-1 too? I'm probably wrong about it, just checking, esp. as
we don't actually have tests for any of the output here!
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