Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6008#discussion_r31370531
--- Diff: docs/mllib-feature-extraction.md ---
@@ -514,16 +514,43 @@ import org.apache.spark.SparkContext._
import org.apache.spark.mllib.feature.ElementwiseProduct
import org.apache.spark.mllib.linalg.Vectors
-// Load and parse the data:
-val data = sc.textFile("data/mllib/kmeans_data.txt")
-val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble)))
+// Create some vector data; also works for sparse vectors
+val data = sc.parallelize(Array(Vectors.dense(1.0, 2.0, 3.0),
Vectors.dense(4.0, 5.0, 6.0)))
val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
val transformer = new ElementwiseProduct(transformingVector)
// Batch transform and per-row transform give the same results:
-val transformedData = transformer.transform(parsedData)
-val transformedData2 = parsedData.map(x => transformer.transform(x))
+val transformedData = transformer.transform(data)
+val transformedData2 = data.map(x => transformer.transform(x))
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java">
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.feature.ElementwiseProduct;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+
+// Create some vector data; also works for sparse vectors
+JavaRDD<Vector> data = sc.parallelize(Arrays.asList(
+ Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)));
+Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0);
+ElementwiseProduct transformer = new
ElementwiseProduct(transformingVector);
+
+// Batch transform and per-row transform give the same results:
+JavaRDD<Vector> transformedData = transformer.transform(data);
+JavaRDD<Vector> transformedData2 = data.map(
+ new Function<Vector, Vector>() {
+ @Override
--- End diff --
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