Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/7522#issuecomment-127150995
[Test build #39533 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/39533/console)
for PR 7522 at commit
[`60dec05`](https://github.com/apache/spark/commit/60dec053e0bd262da63f03d4a35d57a1f915c200).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is a
statistical procedure that uses an orthogonal transformation to convert a set
of observations of possibly correlated variables into a set of values of
linearly uncorrelated variables called principal components. A
[PCA](api/scala/index.html#org.apache.spark.ml.feature.PCA) class trains a
model to project vectors to a low-dimensional space using PCA. The example
below shows how to project 5-dimensional feature vectors into 3-dimensional
principal components.`
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