Github user psuszyns commented on a diff in the pull request:
https://github.com/apache/spark/pull/12419#discussion_r61456369
--- 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 --
This is RowMatrix as in 1.6.1 release:
https://github.com/apache/spark/blob/15de51c238a7340fa81cb0b80d029a05d97bfc5c/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
am I correct? If yes then can you find there a method named
computePrincipalComponentsAndExplainedVariance? I can't, yet on master it is
annotated with Since("1.6.0") - isn't it false?
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