Github user brkyvz commented on a diff in the pull request:
https://github.com/apache/spark/pull/2041#discussion_r16442838
--- Diff: docs/mllib-stats.md ---
@@ -25,7 +25,79 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> -
Statistics Functionality
\newcommand{\zero}{\mathbf{0}}
\]`
-## Data Generators
+## Random data generation
+
+Random data generation is useful for randomized algorithms, prototyping,
and performance testing.
+MLlib supports generating random RDDs with i.i.d. values drawn from a
given distribution:
+uniform, standard normal, or Poisson.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[`RandomRDDs`](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs)
provides factory
+methods to generate random double RDDs or vector RDDs.
--- End diff --
"methods to generate random double RDDs or vector RDDs": should we mention
that a user can extend RandomDataGenerator and generate a random RDD of
whatever custom object they want?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
