Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/10002#discussion_r46080247
--- Diff: docs/ml-features.md ---
@@ -1508,25 +737,7 @@ This example below demonstrates how to transform
vectors using a transforming ve
Refer to the [ElementwiseProduct Scala
docs](api/scala/index.html#org.apache.spark.ml.feature.ElementwiseProduct)
for more details on the API.
-{% highlight scala %}
-import org.apache.spark.ml.feature.ElementwiseProduct
-import org.apache.spark.mllib.linalg.Vectors
-
-// Create some vector data; also works for sparse vectors
-val dataFrame = sqlContext.createDataFrame(Seq(
- ("a", Vectors.dense(1.0, 2.0, 3.0)),
- ("b", Vectors.dense(4.0, 5.0, 6.0)))).toDF("id", "vector")
-
-val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
-val transformer = new ElementwiseProduct()
- .setScalingVec(transformingVector)
- .setInputCol("vector")
- .setOutputCol("transformedVector")
-
-// Batch transform the vectors to create new column:
-transformer.transform(dataFrame).show()
-
-{% endhighlight %}
+{% include_example
scala/org/apache/spark/examples/ml/ElementWiseProductExample.scala %}
--- End diff --
Change the file name into `ElementwiseProductExample.scala`
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