Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7522#discussion_r36045312
--- Diff: docs/ml-features.md ---
@@ -461,6 +461,87 @@ for binarized_feature, in binarizedFeatures.collect():
</div>
</div>
+## PCA
+
+[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.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.PCA
+import org.apache.spark.mllib.linalg.Vectors
+
+val data = Array(
+ Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
+ Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
+ Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
+)
+val df =
sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
+val pca = new PCA()
+ .setInputCol("features")
+ .setOutputCol("pcaFeatures")
+ .setK(3)
+ .fit(df)
+val pcaDF = pca.transform(df)
+val result = pcaDF.select("pcaFeatures")
+result.show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import com.google.common.collect.Lists;
--- End diff --
Just to confirm: Did you run the Java example?
The example should import PCA, PCAModel (since we should not assume it's in
the same namespace).
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