Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3319#discussion_r21925528
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -313,15 +593,145 @@ object Matrices {
* @return Square `DenseMatrix` with size `values.length` x
`values.length` and `values`
* on the diagonal
*/
- def diag(vector: Vector): Matrix = {
- val n = vector.size
- val matrix = Matrices.eye(n)
- val values = vector.toArray
- var i = 0
- while (i < n) {
- matrix.update(i, i, values(i))
- i += 1
+ def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)
+
+ /**
+ * Horizontally concatenate a sequence of matrices. The returned matrix
will be in the format
+ * the matrices are supplied in. Supplying a mix of dense and sparse
matrices will result in
+ * a dense matrix.
--- End diff --
I like the MATLAB approach better. Usually a sparse matrix is very sparse,
while a dense component is quite small, for example,
~~~
A^T A A^T
A I
~~~
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