Github user jodersky commented on a diff in the pull request:
https://github.com/apache/spark/pull/12419#discussion_r61313031
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -379,15 +379,21 @@ class RowMatrix @Since("1.0.0") (
*
* Note that this cannot be computed on matrices with more than 65535
columns.
*
- * @param k number of top principal components.
+ * @param filter either the number of top principal components or
variance
+ * retained by the minimal set of principal components.
* @return a matrix of size n-by-k, whose columns are principal
components, and
* a vector of values which indicate how much variance each principal
component
* explains
*/
@Since("1.6.0")
- def computePrincipalComponentsAndExplainedVariance(k: Int): (Matrix,
Vector) = {
+ def computePrincipalComponentsAndExplainedVariance(filter: Either[Int,
Double])
--- End diff --
I'm no expert in the ML domain, but from a user perspective, this breaks
API backwards compatibility.
An alternative could be to create a new method and factor out common
behaviour shared with the current
`computePrincipalComponentsAndExplainedVariance` into a private utility method.
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