Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4580#discussion_r29816049
--- Diff: docs/mllib-feature-extraction.md ---
@@ -477,3 +477,76 @@ sc.stop();
</div>
</div>
+## ElementwiseProduct
+
+ElementwiseProduct multiplies individual vector samples by a provided
weighting vector component-wise. This represents the [Hadamard
product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between
the input vector, `v` and transforming vector, `w`, to yield a result vector.
+
+`\[ \begin{pmatrix}
+v_1 \\
+\vdots \\
+v_N
+\end{pmatrix} \circ \begin{pmatrix}
+ w_1 \\
+ \vdots \\
+ w_N
+ \end{pmatrix}
+= \begin{pmatrix}
+ v_1 w_1 \\
+ \vdots \\
+ v_N w_N
+ \end{pmatrix}
+\]`
+
+[`ElementwiseProduct`](api/scala/index.html#org.apache.spark.mllib.feature.ElementwiseProduct)
has the following parameter in the constructor:
+
+* `w` Vector, the transforming vector.
+
+`ElementwiseProduct` implements
[`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer)
which can apply the weighting on a `Vector` to produce a transformed `Vector`
or on an `RDD[Vector]` to produce a transformed `RDD[Vector]`.
+
+### Example
+
+This example below demonstrates how to load a simple vectors file, extract
a set of vectors, then transform those vectors using a transforming vector
value.
+
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+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)))
+
+val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
+val transformer = new ElementwiseProduct(transformingVector)
+
+//same results:
--- End diff --
Add space here too: ```// same```
Also, can this doc please be more explicit?
```
// Batch transform and per-row transform give the same results.
```
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