Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8184#discussion_r37246863
--- Diff: docs/ml-features.md ---
@@ -649,6 +649,70 @@ for expanded in polyDF.select("polyFeatures").take(3):
</div>
</div>
+## Discrete Cosine Transform (DCT)
+
+The [Discrete Cosine
Transform](https://en.wikipedia.org/wiki/Discrete_cosine_transform) transforms
a length $N$ real-valued sequence in the time domain into another length $N$
real-valued sequence in the frequency domain. A
[DCT](api/scala/index.html#org.apache.spark.ml.feature.DCT) class provides this
functionality, implementing the
[DCT-II](https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II) and
scaling the result by $1/\sqrt{2}$ such that the representing matrix for the
transform is unitary. No shift is applied to the transformed sequence (e.g. the
$0$th element of the transformed sequence is the $0$th DCT coefficient and
_not_ the $N/2$th).
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.DCT
+import org.apache.spark.mllib.linalg.Vectors
+
+val data = Seq(
+ Vectors.dense(0.0, 1.0, -2.0, 3.0),
+ Vectors.dense(-1.0, 2.0, 4.0, -7.0),
+ Vectors.dense(14.0, -2.0, -5.0, 1.0))
+val df =
sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
+val DCTransform = new DCT()
+ .setInputCol("features")
+ .setOutputCol("featuresDCT")
+ .setInverse(false)
+val DCTdf = DCTransform.transform(df)
+DCTdf.select("featuresDCT").take(3).foreach(println)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import com.google.common.collect.Lists;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.feature.DCT;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
--- End diff --
minor: `Lists.newArrayList` -> `Arrays.asList` (to remove Guava dependency)
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