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diff --git a/docs/ml-features.md b/docs/ml-features.md index 6b2e36b353..28a61933f8 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -649,6 +649,77 @@ 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 dct = new DCT() + .setInputCol("features") + .setOutputCol("featuresDCT") + .setInverse(false) +val dctDf = dct.transform(df) +dctDf.select("featuresDCT").show(3) +{% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> +{% highlight java %} +import java.util.Arrays; + +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(Arrays.asList( + RowFactory.create(Vectors.dense(0.0, 1.0, -2.0, 3.0)), + RowFactory.create(Vectors.dense(-1.0, 2.0, 4.0, -7.0)), + RowFactory.create(Vectors.dense(14.0, -2.0, -5.0, 1.0)) +)); +StructType schema = new StructType(new StructField[] { + new StructField("features", new VectorUDT(), false, Metadata.empty()), +}); +DataFrame df = jsql.createDataFrame(data, schema); +DCT dct = new DCT() + .setInputCol("features") + .setOutputCol("featuresDCT") + .setInverse(false); +DataFrame dctDf = dct.transform(df); +dctDf.select("featuresDCT").show(3); +{% endhighlight %} +</div> +</div> + ## StringIndexer `StringIndexer` encodes a string column of labels to a column of label indices. |