Github user funes commented on a diff in the pull request:
https://github.com/apache/spark/pull/661#discussion_r12311565
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
---
@@ -136,7 +136,7 @@ object Vectors {
new DenseVector(v.toArray) // Can't use underlying array
directly, so make a new one
}
case v: BSV[Double] =>
- new SparseVector(v.length, v.index, v.data)
+ new SparseVector(v.length, v.index.slice(0, v.used),
v.data.slice(0, v.used))
--- End diff --
That makes sense. And I just found that it failed the test "Should not copy
data". SparseVector from VectorBuilder has this problem of size inconsistency.
It uses a dynamic growing approach to resize the index/data array and fills the
unused slot zeros. So I think there is no way to completely avoid new
allocation.
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