[SPARK-3136][MLLIB] Create Java-friendly methods in RandomRDDs

Though we don't use default argument for methods in RandomRDDs, it is still not easy for Java users to use because the output type is either `RDD[Double]` or `RDD[Vector]`. Java users should expect `JavaDoubleRDD` and `JavaRDD[Vector]`, respectively. We should create dedicated methods for Java users, and allow default arguments in Scala methods in RandomRDDs, to make life easier for both Java and Scala users. This PR also contains documentation for random data generation. brkyvz

Author: Xiangrui Meng <meng@databricks.com>

Closes #2041 from mengxr/stat-doc and squashes the following commits:

fc5eedf [Xiangrui Meng] add missing comma
ffde810 [Xiangrui Meng] address comments
aef6d07 [Xiangrui Meng] add doc for random data generation
b99d94b [Xiangrui Meng] add java-friendly methods to RandomRDDs

1 files changed, 10 insertions, 10 deletions

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 3f3b19053d..4dc1a4a912 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -35,10 +35,10 @@ class RandomRDDs:
     def uniformRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the
-        uniform distribution on [0.0, 1.0].
+        uniform distribution U(0.0, 1.0).
 
-        To transform the distribution in the generated RDD from U[0.0, 1.0]
-        to U[a, b], use
+        To transform the distribution in the generated RDD from U(0.0, 1.0)
+        to U(a, b), use
         C{RandomRDDs.uniformRDD(sc, n, p, seed)\
           .map(lambda v: a + (b - a) * v)}
 
@@ -60,11 +60,11 @@ class RandomRDDs:
     @staticmethod
     def normalRDD(sc, size, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of i.i.d samples from the standard normal
+        Generates an RDD comprised of i.i.d. samples from the standard normal
         distribution.
 
         To transform the distribution in the generated RDD from standard normal
-        to some other normal N(mean, sigma), use
+        to some other normal N(mean, sigma^2), use
         C{RandomRDDs.normal(sc, n, p, seed)\
           .map(lambda v: mean + sigma * v)}
 
@@ -84,7 +84,7 @@ class RandomRDDs:
     @staticmethod
     def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of i.i.d samples from the Poisson
+        Generates an RDD comprised of i.i.d. samples from the Poisson
         distribution with the input mean.
 
         >>> mean = 100.0
@@ -105,8 +105,8 @@ class RandomRDDs:
     @staticmethod
     def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of vectors containing i.i.d samples drawn
-        from the uniform distribution on [0.0 1.0].
+        Generates an RDD comprised of vectors containing i.i.d. samples drawn
+        from the uniform distribution U(0.0, 1.0).
 
         >>> import numpy as np
         >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
@@ -125,7 +125,7 @@ class RandomRDDs:
     @staticmethod
     def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of vectors containing i.i.d samples drawn
+        Generates an RDD comprised of vectors containing i.i.d. samples drawn
         from the standard normal distribution.
 
         >>> import numpy as np
@@ -145,7 +145,7 @@ class RandomRDDs:
     @staticmethod
     def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of vectors containing i.i.d samples drawn
+        Generates an RDD comprised of vectors containing i.i.d. samples drawn
         from the Poisson distribution with the input mean.
 
         >>> import numpy as np