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Posted to commits@spark.apache.org by me...@apache.org on 2014/07/03 20:55:10 UTC
git commit: SPARK-1675. Make clear whether computePrincipalComponents
requires centered data
Repository: spark
Updated Branches:
refs/heads/master c48053773 -> 2b36344f5
SPARK-1675. Make clear whether computePrincipalComponents requires centered data
Just closing out this small JIRA, resolving with a comment change.
Author: Sean Owen <so...@cloudera.com>
Closes #1171 from srowen/SPARK-1675 and squashes the following commits:
45ee9b7 [Sean Owen] Add simple note that data need not be centered for computePrincipalComponents
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2b36344f
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2b36344f
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2b36344f
Branch: refs/heads/master
Commit: 2b36344f588d4e7357ce9921dc656e2389ba1dea
Parents: c480537
Author: Sean Owen <so...@cloudera.com>
Authored: Thu Jul 3 11:54:51 2014 -0700
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Thu Jul 3 11:54:51 2014 -0700
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.../org/apache/spark/mllib/linalg/distributed/RowMatrix.scala | 2 ++
1 file changed, 2 insertions(+)
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http://git-wip-us.apache.org/repos/asf/spark/blob/2b36344f/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
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diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 1a0073c..695e03b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -347,6 +347,8 @@ class RowMatrix(
* The principal components are stored a local matrix of size n-by-k.
* Each column corresponds for one principal component,
* and the columns are in descending order of component variance.
+ * The row data do not need to be "centered" first; it is not necessary for
+ * the mean of each column to be 0.
*
* @param k number of top principal components.
* @return a matrix of size n-by-k, whose columns are principal components