Github user feynmanliang commented on a diff in the pull request: https://github.com/apache/spark/pull/8184#discussion_r37248201 --- 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 -- OK. I've updated this PR and created a starter JIRA at SPARK-10070 to do this across all user guide
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