Hi, junjie.
As Nick said,
spark.ml indeed contains Vector, Vectors and VectorUDT by itself, see:
mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala:36:
sealed trait Vector extends Serializable
So, which bug do you find with VectorAssembler? Could you give more details?
There are Vector classes under ml.linalg package - And VectorAssembler and
other feature transformers all work with ml.linalg vectors.
If you try to use mllib.linalg vectors instead you will get an error as the
user defined type for SQL is not correct
On Thu, 13 Jul 2017 at 11:23,
Dear Developers:
Here is a bug in org.apache.spark.ml.linalg.*:
Class Vector, Vectors are not included in org.apache.spark.ml.linalg.*,
but they are used in VectorAssembler.scala as follows:
import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
Therefore, bug was reported when I was
