add doc for random data generation

Author: mengxr

docs/mllib-guide.md

@@ -9,7 +9,7 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv
 
 * [Data types](mllib-basics.html)
 * [Basic statistics](mllib-stats.html)
-  * data generators  
+  * random data generation  
   * stratified sampling
   * summary statistics
   * hypothesis testing

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.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% 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 %}
+</div>
+
+<div data-lang="java" markdown="1">
+[`RandomRDDs`](api/java/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight java %}
+import org.apache.spark.SparkContext;
+import org.apache.spark.api.JavaDoubleRDD;
+import static org.apache.spark.mllib.random.RandomRDDs.*;
+
+JavaSparkContext jsc = ...
+
+// 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.
+JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10);
+// Apply a transform to get a random double RDD following `N(1, 4)`.
+JavaDoubleRDD v = u.map(
+  new Function<Double, Double>() {
+    public Double call(Double x) {
+      return 1.0 + 2.0 * x;
+    }
+  });
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight python %}
+from pyspark.mllib.random import RandomRDDs
+
+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.
+u = RandomRDDs.uniformRDD(sc, 1000000L, 10)
+# Apply a transform to get a random double RDD following `N(1, 4)`.
+v = u.map(lambda x: 1.0 + 2.0 * x)
+{% endhighlight %}
+</div>
+
+</div>
 
 ## Stratified Sampling