Github user rezazadeh commented on a diff in the pull request:
https://github.com/apache/spark/pull/1778#discussion_r17818718
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
---
@@ -53,4 +53,14 @@ trait MultivariateStatisticalSummary {
* Minimum value of each column.
*/
def min: Vector
+
+ /**
+ * Euclidean magnitude of each column
+ */
+ def normL2: Vector
+
+ /**
+ * L1 norm of each column
+ */
+ def normL1: Vector
--- End diff --
For general vectors and matrices, L1 and L2 norms are widely accepted as
summaries of a vector and are standard linear algebra:
http://en.wikipedia.org/wiki/Norm_(mathematics)#p-norm
`RowMatrix` is a general matrix, not necessarily holding random variable
samples in its columns.
By using the names `normL1` and `normL2` it is clear what we're talking
about - there is no mistaking what L1 means. Whereas if I were to name them
`norm1` and use some averaging, I think the name is ambiguous and requires the
user to know we're assuming the matrix holds random variable samples in its
columns. No need to confuse the user to make an assumption we don't need.
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