Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3319#discussion_r22063271
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -256,72 +524,297 @@ object Matrices {
* Generate a `DenseMatrix` consisting of zeros.
* @param numRows number of rows of the matrix
* @param numCols number of columns of the matrix
- * @return `DenseMatrix` with size `numRows` x `numCols` and values of
zeros
+ * @return `Matrix` with size `numRows` x `numCols` and values of zeros
*/
- def zeros(numRows: Int, numCols: Int): Matrix =
- new DenseMatrix(numRows, numCols, new Array[Double](numRows * numCols))
+ def zeros(numRows: Int, numCols: Int): Matrix =
DenseMatrix.zeros(numRows, numCols)
--- End diff --
I see your point. The reason we didn't return the exact type in `Vectors`
and `Matrices` was because RDD is not covariant. But maybe we should return the
exact types that and let algorithms take a generic `RDD[T]` with `T` extending
`Vector`.
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