Github user dusenberrymw commented on the pull request:
https://github.com/apache/spark/pull/7554#issuecomment-125269473
@MechCoder Thanks for the thoughts! I too have been working on changes for
the past few days since I last pushed updates, and I have pushed those changes
now. Basically, I now have `RowMatrix`, `IndexedRowMatrix`, and
`CoordinateMatrix` classes that each take an RDD of `rows` (or `entries` for a
`CoordinateMatrix`), optional `numRows`, and optional `numCols`. For matrix
conversions, each has a `_from_java` function that can create a PySpark version
of a given Java distributed matrix object, which aids in matrix conversions.
So, if a PySpark `CoordinateMatrix` needs to be converted to a `RowMatrix`, it
will first get the resulting `RowMatrix` Java object, then call
`RowMatrix._from_java(javaRowMatrix)`, which will then create a PySpark
`RowMatrix`.
I think we will want to stay away from having both a `RowMatrix` and a
`RowMatrixModel`, especially since a user that converts a `CoordinateMatrix` to
a `RowMatrix` will be expecting a `RowMatrix`. The design I've pushed should
keep the API simple and in line with the Scala/Java side.
Interested in any thoughts you may have still!
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