Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11108#discussion_r55145679
--- Diff: docs/mllib-statistics.md ---
@@ -562,18 +277,7 @@ distribution `N(0, 1)`, and then map it to `N(1, 4)`.
Refer to the [`RandomRDDs` Scala
docs](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs) for
details on the API.
-{% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.mllib.random.RandomRDDs._
-
-val sc: SparkContext = ...
-
-// Generate a random double RDD that contains 1 million i.i.d. values
drawn from the
-// standard normal distribution `N(0, 1)`, evenly distributed in 10
partitions.
-val u = normalRDD(sc, 1000000L, 10)
-// Apply a transform to get a random double RDD following `N(1, 4)`.
-val v = u.map(x => 1.0 + 2.0 * x)
-{% endhighlight %}
+{% include_example
scala/org/apache/spark/examples/mllib/RandomDataGenerationExample.scala %}
--- End diff --
Let's keep the random data generation examples here without substitution,
since the example is quite simple. Same for `JavaRandomDataGenerationExample`
and `random_data_generation_example`
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