RandomRDDs (Spark 2.3.3 JavaDoc)

Object
- org.apache.spark.mllib.random.RandomRDDs

```
public class RandomRDDs
extends Object
```
Generator methods for creating RDDs comprised of i.i.d. samples from some distribution.

Constructor Summary

Constructors
Constructor and Description

RandomRDDs()

Constructors
Constructor and Description
`RandomRDDs()`

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method and Description
`static JavaDoubleRDD`	`exponentialJavaRDD(JavaSparkContext jsc, double mean, long size)` `RandomRDDs.exponentialJavaRDD` with the default number of partitions and the default seed.
`static JavaDoubleRDD`	`exponentialJavaRDD(JavaSparkContext jsc, double mean, long size, int numPartitions)` `RandomRDDs.exponentialJavaRDD` with the default seed.
`static JavaDoubleRDD`	`exponentialJavaRDD(JavaSparkContext jsc, double mean, long size, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.exponentialRDD`.
`static JavaRDD<Vector>`	`exponentialJavaVectorRDD(JavaSparkContext jsc, double mean, long numRows, int numCols)` `RandomRDDs.exponentialJavaVectorRDD` with the default number of partitions and the default seed.
`static JavaRDD<Vector>`	`exponentialJavaVectorRDD(JavaSparkContext jsc, double mean, long numRows, int numCols, int numPartitions)` `RandomRDDs.exponentialJavaVectorRDD` with the default seed.
`static JavaRDD<Vector>`	`exponentialJavaVectorRDD(JavaSparkContext jsc, double mean, long numRows, int numCols, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.exponentialVectorRDD`.
`static RDD<Object>`	`exponentialRDD(SparkContext sc, double mean, long size, int numPartitions, long seed)` Generates an RDD comprised of `i.i.d.` samples from the exponential distribution with the input mean.
`static RDD<Vector>`	`exponentialVectorRDD(SparkContext sc, double mean, long numRows, int numCols, int numPartitions, long seed)` Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the exponential distribution with the input mean.
`static JavaDoubleRDD`	`gammaJavaRDD(JavaSparkContext jsc, double shape, double scale, long size)` `RandomRDDs.gammaJavaRDD` with the default number of partitions and the default seed.
`static JavaDoubleRDD`	`gammaJavaRDD(JavaSparkContext jsc, double shape, double scale, long size, int numPartitions)` `RandomRDDs.gammaJavaRDD` with the default seed.
`static JavaDoubleRDD`	`gammaJavaRDD(JavaSparkContext jsc, double shape, double scale, long size, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.gammaRDD`.
`static JavaRDD<Vector>`	`gammaJavaVectorRDD(JavaSparkContext jsc, double shape, double scale, long numRows, int numCols)` `RandomRDDs.gammaJavaVectorRDD` with the default number of partitions and the default seed.
`static JavaRDD<Vector>`	`gammaJavaVectorRDD(JavaSparkContext jsc, double shape, double scale, long numRows, int numCols, int numPartitions)` `RandomRDDs.gammaJavaVectorRDD` with the default seed.
`static JavaRDD<Vector>`	`gammaJavaVectorRDD(JavaSparkContext jsc, double shape, double scale, long numRows, int numCols, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.gammaVectorRDD`.
`static RDD<Object>`	`gammaRDD(SparkContext sc, double shape, double scale, long size, int numPartitions, long seed)` Generates an RDD comprised of `i.i.d.` samples from the gamma distribution with the input shape and scale.
`static RDD<Vector>`	`gammaVectorRDD(SparkContext sc, double shape, double scale, long numRows, int numCols, int numPartitions, long seed)` Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the gamma distribution with the input shape and scale.
`static JavaDoubleRDD`	`logNormalJavaRDD(JavaSparkContext jsc, double mean, double std, long size)` `RandomRDDs.logNormalJavaRDD` with the default number of partitions and the default seed.
`static JavaDoubleRDD`	`logNormalJavaRDD(JavaSparkContext jsc, double mean, double std, long size, int numPartitions)` `RandomRDDs.logNormalJavaRDD` with the default seed.
`static JavaDoubleRDD`	`logNormalJavaRDD(JavaSparkContext jsc, double mean, double std, long size, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.logNormalRDD`.
`static JavaRDD<Vector>`	`logNormalJavaVectorRDD(JavaSparkContext jsc, double mean, double std, long numRows, int numCols)` `RandomRDDs.logNormalJavaVectorRDD` with the default number of partitions and the default seed.
`static JavaRDD<Vector>`	`logNormalJavaVectorRDD(JavaSparkContext jsc, double mean, double std, long numRows, int numCols, int numPartitions)` `RandomRDDs.logNormalJavaVectorRDD` with the default seed.
`static JavaRDD<Vector>`	`logNormalJavaVectorRDD(JavaSparkContext jsc, double mean, double std, long numRows, int numCols, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.logNormalVectorRDD`.
`static RDD<Object>`	`logNormalRDD(SparkContext sc, double mean, double std, long size, int numPartitions, long seed)` Generates an RDD comprised of `i.i.d.` samples from the log normal distribution with the input mean and standard deviation
`static RDD<Vector>`	`logNormalVectorRDD(SparkContext sc, double mean, double std, long numRows, int numCols, int numPartitions, long seed)` Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from a log normal distribution.
`static JavaDoubleRDD`	`normalJavaRDD(JavaSparkContext jsc, long size)` `RandomRDDs.normalJavaRDD` with the default number of partitions and the default seed.
`static JavaDoubleRDD`	`normalJavaRDD(JavaSparkContext jsc, long size, int numPartitions)` `RandomRDDs.normalJavaRDD` with the default seed.
`static JavaDoubleRDD`	`normalJavaRDD(JavaSparkContext jsc, long size, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.normalRDD`.
`static JavaRDD<Vector>`	`normalJavaVectorRDD(JavaSparkContext jsc, long numRows, int numCols)` `RandomRDDs.normalJavaVectorRDD` with the default number of partitions and the default seed.
`static JavaRDD<Vector>`	`normalJavaVectorRDD(JavaSparkContext jsc, long numRows, int numCols, int numPartitions)` `RandomRDDs.normalJavaVectorRDD` with the default seed.
`static JavaRDD<Vector>`	`normalJavaVectorRDD(JavaSparkContext jsc, long numRows, int numCols, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.normalVectorRDD`.
`static RDD<Object>`	`normalRDD(SparkContext sc, long size, int numPartitions, long seed)` Generates an RDD comprised of `i.i.d.` samples from the standard normal distribution.
`static RDD<Vector>`	`normalVectorRDD(SparkContext sc, long numRows, int numCols, int numPartitions, long seed)` Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the standard normal distribution.
`static JavaDoubleRDD`	`poissonJavaRDD(JavaSparkContext jsc, double mean, long size)` `RandomRDDs.poissonJavaRDD` with the default number of partitions and the default seed.
`static JavaDoubleRDD`	`poissonJavaRDD(JavaSparkContext jsc, double mean, long size, int numPartitions)` `RandomRDDs.poissonJavaRDD` with the default seed.
`static JavaDoubleRDD`	`poissonJavaRDD(JavaSparkContext jsc, double mean, long size, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.poissonRDD`.
`static JavaRDD<Vector>`	`poissonJavaVectorRDD(JavaSparkContext jsc, double mean, long numRows, int numCols)` `RandomRDDs.poissonJavaVectorRDD` with the default number of partitions and the default seed.
`static JavaRDD<Vector>`	`poissonJavaVectorRDD(JavaSparkContext jsc, double mean, long numRows, int numCols, int numPartitions)` `RandomRDDs.poissonJavaVectorRDD` with the default seed.
`static JavaRDD<Vector>`	`poissonJavaVectorRDD(JavaSparkContext jsc, double mean, long numRows, int numCols, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.poissonVectorRDD`.
`static RDD<Object>`	`poissonRDD(SparkContext sc, double mean, long size, int numPartitions, long seed)` Generates an RDD comprised of `i.i.d.` samples from the Poisson distribution with the input mean.
`static RDD<Vector>`	`poissonVectorRDD(SparkContext sc, double mean, long numRows, int numCols, int numPartitions, long seed)` Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the Poisson distribution with the input mean.
`static <T> JavaRDD<T>`	`randomJavaRDD(JavaSparkContext jsc, RandomDataGenerator<T> generator, long size)` :: DeveloperApi :: `RandomRDDs.randomJavaRDD` with the default seed & numPartitions
`static <T> JavaRDD<T>`	`randomJavaRDD(JavaSparkContext jsc, RandomDataGenerator<T> generator, long size, int numPartitions)` :: DeveloperApi :: `RandomRDDs.randomJavaRDD` with the default seed.
`static <T> JavaRDD<T>`	`randomJavaRDD(JavaSparkContext jsc, RandomDataGenerator<T> generator, long size, int numPartitions, long seed)` :: DeveloperApi :: Generates an RDD comprised of `i.i.d.` samples produced by the input RandomDataGenerator.
`static JavaRDD<Vector>`	`randomJavaVectorRDD(JavaSparkContext jsc, RandomDataGenerator<Object> generator, long numRows, int numCols)` :: DeveloperApi :: `RandomRDDs.randomJavaVectorRDD` with the default number of partitions and the default seed.
`static JavaRDD<Vector>`	`randomJavaVectorRDD(JavaSparkContext jsc, RandomDataGenerator<Object> generator, long numRows, int numCols, int numPartitions)` :: DeveloperApi :: `RandomRDDs.randomJavaVectorRDD` with the default seed.
`static JavaRDD<Vector>`	`randomJavaVectorRDD(JavaSparkContext jsc, RandomDataGenerator<Object> generator, long numRows, int numCols, int numPartitions, long seed)` :: DeveloperApi :: Java-friendly version of `RandomRDDs.randomVectorRDD`.
`static <T> RDD<T>`	`randomRDD(SparkContext sc, RandomDataGenerator<T> generator, long size, int numPartitions, long seed, scala.reflect.ClassTag<T> evidence$1)` :: DeveloperApi :: Generates an RDD comprised of `i.i.d.` samples produced by the input RandomDataGenerator.
`static RDD<Vector>`	`randomVectorRDD(SparkContext sc, RandomDataGenerator<Object> generator, long numRows, int numCols, int numPartitions, long seed)` :: DeveloperApi :: Generates an RDD[Vector] with vectors containing `i.i.d.` samples produced by the input RandomDataGenerator.
`static JavaDoubleRDD`	`uniformJavaRDD(JavaSparkContext jsc, long size)` `RandomRDDs.uniformJavaRDD` with the default number of partitions and the default seed.
`static JavaDoubleRDD`	`uniformJavaRDD(JavaSparkContext jsc, long size, int numPartitions)` `RandomRDDs.uniformJavaRDD` with the default seed.
`static JavaDoubleRDD`	`uniformJavaRDD(JavaSparkContext jsc, long size, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.uniformRDD`.
`static JavaRDD<Vector>`	`uniformJavaVectorRDD(JavaSparkContext jsc, long numRows, int numCols)` `RandomRDDs.uniformJavaVectorRDD` with the default number of partitions and the default seed.
`static JavaRDD<Vector>`	`uniformJavaVectorRDD(JavaSparkContext jsc, long numRows, int numCols, int numPartitions)` `RandomRDDs.uniformJavaVectorRDD` with the default seed.
`static JavaRDD<Vector>`	`uniformJavaVectorRDD(JavaSparkContext jsc, long numRows, int numCols, int numPartitions, long seed)` Java-friendly version of `RandomRDDs.uniformVectorRDD`.
`static RDD<Object>`	`uniformRDD(SparkContext sc, long size, int numPartitions, long seed)` Generates an RDD comprised of `i.i.d.` samples from the uniform distribution `U(0.0, 1.0)`.
`static RDD<Vector>`	`uniformVectorRDD(SparkContext sc, long numRows, int numCols, int numPartitions, long seed)` Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the uniform distribution on `U(0.0, 1.0)`.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail
- RandomRDDs
```
public RandomRDDs()
```

Method Detail

uniformRDD
```
public static RDD<Object> uniformRDD(SparkContext sc,
                                     long size,
                                     int numPartitions,
                                     long seed)
```
Generates an RDD comprised of i.i.d. samples from the 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 RandomRDDs.uniformRDD(sc, n, p, seed).map(v => a + (b - a) * v).

Parameters:

sc - SparkContext used to create the RDD.

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Double] comprised of i.i.d. samples ~ U(0.0, 1.0).

uniformJavaRDD

public static JavaDoubleRDD uniformJavaRDD(JavaSparkContext jsc,
                                           long size,
                                           int numPartitions,
                                           long seed)

Java-friendly version of RandomRDDs.uniformRDD.

Parameters:: jsc - (undocumented); size - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

uniformJavaRDD

public static JavaDoubleRDD uniformJavaRDD(JavaSparkContext jsc,
                                           long size,
                                           int numPartitions)

RandomRDDs.uniformJavaRDD with the default seed.

Parameters:: jsc - (undocumented); size - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

uniformJavaRDD
```
public static JavaDoubleRDD uniformJavaRDD(JavaSparkContext jsc,
                                           long size)
```
RandomRDDs.uniformJavaRDD with the default number of partitions and the default seed.

Parameters:

jsc - (undocumented)

size - (undocumented)

Returns:

(undocumented)

normalRDD
```
public static RDD<Object> normalRDD(SparkContext sc,
                                    long size,
                                    int numPartitions,
                                    long seed)
```
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^2^), use RandomRDDs.normalRDD(sc, n, p, seed).map(v => mean + sigma * v).

Parameters:

sc - SparkContext used to create the RDD.

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Double] comprised of i.i.d. samples ~ N(0.0, 1.0).

normalJavaRDD

public static JavaDoubleRDD normalJavaRDD(JavaSparkContext jsc,
                                          long size,
                                          int numPartitions,
                                          long seed)

Java-friendly version of RandomRDDs.normalRDD.

Parameters:: jsc - (undocumented); size - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

normalJavaRDD

public static JavaDoubleRDD normalJavaRDD(JavaSparkContext jsc,
                                          long size,
                                          int numPartitions)

RandomRDDs.normalJavaRDD with the default seed.

Parameters:: jsc - (undocumented); size - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

normalJavaRDD
```
public static JavaDoubleRDD normalJavaRDD(JavaSparkContext jsc,
                                          long size)
```
RandomRDDs.normalJavaRDD with the default number of partitions and the default seed.

Parameters:

jsc - (undocumented)

size - (undocumented)

Returns:

(undocumented)

poissonRDD
```
public static RDD<Object> poissonRDD(SparkContext sc,
                                     double mean,
                                     long size,
                                     int numPartitions,
                                     long seed)
```
Generates an RDD comprised of i.i.d. samples from the Poisson distribution with the input mean.

Parameters:

sc - SparkContext used to create the RDD.

mean - Mean, or lambda, for the Poisson distribution.

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Double] comprised of i.i.d. samples ~ Pois(mean).

poissonJavaRDD

public static JavaDoubleRDD poissonJavaRDD(JavaSparkContext jsc,
                                           double mean,
                                           long size,
                                           int numPartitions,
                                           long seed)

Java-friendly version of RandomRDDs.poissonRDD.

Parameters:: jsc - (undocumented); mean - (undocumented); size - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

poissonJavaRDD

public static JavaDoubleRDD poissonJavaRDD(JavaSparkContext jsc,
                                           double mean,
                                           long size,
                                           int numPartitions)

RandomRDDs.poissonJavaRDD with the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); size - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

poissonJavaRDD
```
public static JavaDoubleRDD poissonJavaRDD(JavaSparkContext jsc,
                                           double mean,
                                           long size)
```
RandomRDDs.poissonJavaRDD with the default number of partitions and the default seed.

Parameters:

jsc - (undocumented)

mean - (undocumented)

size - (undocumented)

Returns:

(undocumented)

exponentialRDD
```
public static RDD<Object> exponentialRDD(SparkContext sc,
                                         double mean,
                                         long size,
                                         int numPartitions,
                                         long seed)
```
Generates an RDD comprised of i.i.d. samples from the exponential distribution with the input mean.

Parameters:

sc - SparkContext used to create the RDD.

mean - Mean, or 1 / lambda, for the exponential distribution.

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Double] comprised of i.i.d. samples ~ Pois(mean).

exponentialJavaRDD

public static JavaDoubleRDD exponentialJavaRDD(JavaSparkContext jsc,
                                               double mean,
                                               long size,
                                               int numPartitions,
                                               long seed)

Java-friendly version of RandomRDDs.exponentialRDD.

Parameters:: jsc - (undocumented); mean - (undocumented); size - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

exponentialJavaRDD

public static JavaDoubleRDD exponentialJavaRDD(JavaSparkContext jsc,
                                               double mean,
                                               long size,
                                               int numPartitions)

RandomRDDs.exponentialJavaRDD with the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); size - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

exponentialJavaRDD
```
public static JavaDoubleRDD exponentialJavaRDD(JavaSparkContext jsc,
                                               double mean,
                                               long size)
```
RandomRDDs.exponentialJavaRDD with the default number of partitions and the default seed.

Parameters:

jsc - (undocumented)

mean - (undocumented)

size - (undocumented)

Returns:

(undocumented)

gammaRDD
```
public static RDD<Object> gammaRDD(SparkContext sc,
                                   double shape,
                                   double scale,
                                   long size,
                                   int numPartitions,
                                   long seed)
```
Generates an RDD comprised of i.i.d. samples from the gamma distribution with the input shape and scale.

Parameters:

sc - SparkContext used to create the RDD.

shape - shape parameter (greater than 0) for the gamma distribution

scale - scale parameter (greater than 0) for the gamma distribution

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Double] comprised of i.i.d. samples ~ Pois(mean).

gammaJavaRDD

public static JavaDoubleRDD gammaJavaRDD(JavaSparkContext jsc,
                                         double shape,
                                         double scale,
                                         long size,
                                         int numPartitions,
                                         long seed)

Java-friendly version of RandomRDDs.gammaRDD.

Parameters:: jsc - (undocumented); shape - (undocumented); scale - (undocumented); size - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

gammaJavaRDD

public static JavaDoubleRDD gammaJavaRDD(JavaSparkContext jsc,
                                         double shape,
                                         double scale,
                                         long size,
                                         int numPartitions)

RandomRDDs.gammaJavaRDD with the default seed.

Parameters:: jsc - (undocumented); shape - (undocumented); scale - (undocumented); size - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

gammaJavaRDD

public static JavaDoubleRDD gammaJavaRDD(JavaSparkContext jsc,
                                         double shape,
                                         double scale,
                                         long size)

RandomRDDs.gammaJavaRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); shape - (undocumented); scale - (undocumented); size - (undocumented)
Returns:: (undocumented)

logNormalRDD
```
public static RDD<Object> logNormalRDD(SparkContext sc,
                                       double mean,
                                       double std,
                                       long size,
                                       int numPartitions,
                                       long seed)
```
Generates an RDD comprised of i.i.d. samples from the log normal distribution with the input mean and standard deviation

Parameters:

sc - SparkContext used to create the RDD.

mean - mean for the log normal distribution

std - standard deviation for the log normal distribution

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Double] comprised of i.i.d. samples ~ Pois(mean).

logNormalJavaRDD

public static JavaDoubleRDD logNormalJavaRDD(JavaSparkContext jsc,
                                             double mean,
                                             double std,
                                             long size,
                                             int numPartitions,
                                             long seed)

Java-friendly version of RandomRDDs.logNormalRDD.

Parameters:: jsc - (undocumented); mean - (undocumented); std - (undocumented); size - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

logNormalJavaRDD

public static JavaDoubleRDD logNormalJavaRDD(JavaSparkContext jsc,
                                             double mean,
                                             double std,
                                             long size,
                                             int numPartitions)

RandomRDDs.logNormalJavaRDD with the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); std - (undocumented); size - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

logNormalJavaRDD

public static JavaDoubleRDD logNormalJavaRDD(JavaSparkContext jsc,
                                             double mean,
                                             double std,
                                             long size)

RandomRDDs.logNormalJavaRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); std - (undocumented); size - (undocumented)
Returns:: (undocumented)

randomRDD
```
public static <T> RDD<T> randomRDD(SparkContext sc,
                                   RandomDataGenerator<T> generator,
                                   long size,
                                   int numPartitions,
                                   long seed,
                                   scala.reflect.ClassTag<T> evidence$1)
```
:: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.

Parameters:

sc - SparkContext used to create the RDD.

generator - RandomDataGenerator used to populate the RDD.

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

evidence$1 - (undocumented)

Returns:

RDD[T] comprised of i.i.d. samples produced by generator.

randomJavaRDD
```
public static <T> JavaRDD<T> randomJavaRDD(JavaSparkContext jsc,
                                           RandomDataGenerator<T> generator,
                                           long size,
                                           int numPartitions,
                                           long seed)
```
:: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.

Parameters:

jsc - JavaSparkContext used to create the RDD.

generator - RandomDataGenerator used to populate the RDD.

size - Size of the RDD.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[T] comprised of i.i.d. samples produced by generator.

randomJavaRDD

public static <T> JavaRDD<T> randomJavaRDD(JavaSparkContext jsc,
                                           RandomDataGenerator<T> generator,
                                           long size,
                                           int numPartitions)

:: DeveloperApi :: RandomRDDs.randomJavaRDD with the default seed.

Parameters:: jsc - (undocumented); generator - (undocumented); size - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

randomJavaRDD

public static <T> JavaRDD<T> randomJavaRDD(JavaSparkContext jsc,
                                           RandomDataGenerator<T> generator,
                                           long size)

:: DeveloperApi :: RandomRDDs.randomJavaRDD with the default seed & numPartitions

Parameters:: jsc - (undocumented); generator - (undocumented); size - (undocumented)
Returns:: (undocumented)

uniformVectorRDD
```
public static RDD<Vector> uniformVectorRDD(SparkContext sc,
                                           long numRows,
                                           int numCols,
                                           int numPartitions,
                                           long seed)
```
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the uniform distribution on U(0.0, 1.0).

Parameters:

sc - SparkContext used to create the RDD.

numRows - Number of Vectors in the RDD.

numCols - Number of elements in each Vector.

numPartitions - Number of partitions in the RDD.

seed - Seed for the RNG that generates the seed for the generator in each partition.

Returns:

RDD[Vector] with vectors containing i.i.d samples ~ U(0.0, 1.0).

uniformJavaVectorRDD

public static JavaRDD<Vector> uniformJavaVectorRDD(JavaSparkContext jsc,
                                                   long numRows,
                                                   int numCols,
                                                   int numPartitions,
                                                   long seed)

Java-friendly version of RandomRDDs.uniformVectorRDD.

Parameters:: jsc - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

uniformJavaVectorRDD

public static JavaRDD<Vector> uniformJavaVectorRDD(JavaSparkContext jsc,
                                                   long numRows,
                                                   int numCols,
                                                   int numPartitions)

RandomRDDs.uniformJavaVectorRDD with the default seed.

Parameters:: jsc - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

uniformJavaVectorRDD

public static JavaRDD<Vector> uniformJavaVectorRDD(JavaSparkContext jsc,
                                                   long numRows,
                                                   int numCols)

RandomRDDs.uniformJavaVectorRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); numRows - (undocumented); numCols - (undocumented)
Returns:: (undocumented)

normalVectorRDD
```
public static RDD<Vector> normalVectorRDD(SparkContext sc,
                                          long numRows,
                                          int numCols,
                                          int numPartitions,
                                          long seed)
```
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the standard normal distribution.

Parameters:

sc - SparkContext used to create the RDD.

numRows - Number of Vectors in the RDD.

numCols - Number of elements in each Vector.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Vector] with vectors containing i.i.d. samples ~ N(0.0, 1.0).

normalJavaVectorRDD

public static JavaRDD<Vector> normalJavaVectorRDD(JavaSparkContext jsc,
                                                  long numRows,
                                                  int numCols,
                                                  int numPartitions,
                                                  long seed)

Java-friendly version of RandomRDDs.normalVectorRDD.

Parameters:: jsc - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

normalJavaVectorRDD

public static JavaRDD<Vector> normalJavaVectorRDD(JavaSparkContext jsc,
                                                  long numRows,
                                                  int numCols,
                                                  int numPartitions)

RandomRDDs.normalJavaVectorRDD with the default seed.

Parameters:: jsc - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

normalJavaVectorRDD

public static JavaRDD<Vector> normalJavaVectorRDD(JavaSparkContext jsc,
                                                  long numRows,
                                                  int numCols)

RandomRDDs.normalJavaVectorRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); numRows - (undocumented); numCols - (undocumented)
Returns:: (undocumented)

logNormalVectorRDD
```
public static RDD<Vector> logNormalVectorRDD(SparkContext sc,
                                             double mean,
                                             double std,
                                             long numRows,
                                             int numCols,
                                             int numPartitions,
                                             long seed)
```
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from a log normal distribution.

Parameters:

sc - SparkContext used to create the RDD.

mean - Mean of the log normal distribution.

std - Standard deviation of the log normal distribution.

numRows - Number of Vectors in the RDD.

numCols - Number of elements in each Vector.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Vector] with vectors containing i.i.d. samples.

logNormalJavaVectorRDD

public static JavaRDD<Vector> logNormalJavaVectorRDD(JavaSparkContext jsc,
                                                     double mean,
                                                     double std,
                                                     long numRows,
                                                     int numCols,
                                                     int numPartitions,
                                                     long seed)

Java-friendly version of RandomRDDs.logNormalVectorRDD.

Parameters:: jsc - (undocumented); mean - (undocumented); std - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

logNormalJavaVectorRDD

public static JavaRDD<Vector> logNormalJavaVectorRDD(JavaSparkContext jsc,
                                                     double mean,
                                                     double std,
                                                     long numRows,
                                                     int numCols,
                                                     int numPartitions)

RandomRDDs.logNormalJavaVectorRDD with the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); std - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

logNormalJavaVectorRDD

public static JavaRDD<Vector> logNormalJavaVectorRDD(JavaSparkContext jsc,
                                                     double mean,
                                                     double std,
                                                     long numRows,
                                                     int numCols)

RandomRDDs.logNormalJavaVectorRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); std - (undocumented); numRows - (undocumented); numCols - (undocumented)
Returns:: (undocumented)

poissonVectorRDD
```
public static RDD<Vector> poissonVectorRDD(SparkContext sc,
                                           double mean,
                                           long numRows,
                                           int numCols,
                                           int numPartitions,
                                           long seed)
```
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the Poisson distribution with the input mean.

Parameters:

sc - SparkContext used to create the RDD.

mean - Mean, or lambda, for the Poisson distribution.

numRows - Number of Vectors in the RDD.

numCols - Number of elements in each Vector.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism)

seed - Random seed (default: a random long integer).

Returns:

RDD[Vector] with vectors containing i.i.d. samples ~ Pois(mean).

poissonJavaVectorRDD

public static JavaRDD<Vector> poissonJavaVectorRDD(JavaSparkContext jsc,
                                                   double mean,
                                                   long numRows,
                                                   int numCols,
                                                   int numPartitions,
                                                   long seed)

Java-friendly version of RandomRDDs.poissonVectorRDD.

Parameters:: jsc - (undocumented); mean - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

poissonJavaVectorRDD

public static JavaRDD<Vector> poissonJavaVectorRDD(JavaSparkContext jsc,
                                                   double mean,
                                                   long numRows,
                                                   int numCols,
                                                   int numPartitions)

RandomRDDs.poissonJavaVectorRDD with the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

poissonJavaVectorRDD

public static JavaRDD<Vector> poissonJavaVectorRDD(JavaSparkContext jsc,
                                                   double mean,
                                                   long numRows,
                                                   int numCols)

RandomRDDs.poissonJavaVectorRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); numRows - (undocumented); numCols - (undocumented)
Returns:: (undocumented)

exponentialVectorRDD
```
public static RDD<Vector> exponentialVectorRDD(SparkContext sc,
                                               double mean,
                                               long numRows,
                                               int numCols,
                                               int numPartitions,
                                               long seed)
```
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the exponential distribution with the input mean.

Parameters:

sc - SparkContext used to create the RDD.

mean - Mean, or 1 / lambda, for the Exponential distribution.

numRows - Number of Vectors in the RDD.

numCols - Number of elements in each Vector.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism)

seed - Random seed (default: a random long integer).

Returns:

RDD[Vector] with vectors containing i.i.d. samples ~ Exp(mean).

exponentialJavaVectorRDD

public static JavaRDD<Vector> exponentialJavaVectorRDD(JavaSparkContext jsc,
                                                       double mean,
                                                       long numRows,
                                                       int numCols,
                                                       int numPartitions,
                                                       long seed)

Java-friendly version of RandomRDDs.exponentialVectorRDD.

Parameters:: jsc - (undocumented); mean - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

exponentialJavaVectorRDD

public static JavaRDD<Vector> exponentialJavaVectorRDD(JavaSparkContext jsc,
                                                       double mean,
                                                       long numRows,
                                                       int numCols,
                                                       int numPartitions)

RandomRDDs.exponentialJavaVectorRDD with the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

exponentialJavaVectorRDD

public static JavaRDD<Vector> exponentialJavaVectorRDD(JavaSparkContext jsc,
                                                       double mean,
                                                       long numRows,
                                                       int numCols)

RandomRDDs.exponentialJavaVectorRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); mean - (undocumented); numRows - (undocumented); numCols - (undocumented)
Returns:: (undocumented)

gammaVectorRDD
```
public static RDD<Vector> gammaVectorRDD(SparkContext sc,
                                         double shape,
                                         double scale,
                                         long numRows,
                                         int numCols,
                                         int numPartitions,
                                         long seed)
```
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the gamma distribution with the input shape and scale.

Parameters:

sc - SparkContext used to create the RDD.

shape - shape parameter (greater than 0) for the gamma distribution.

scale - scale parameter (greater than 0) for the gamma distribution.

numRows - Number of Vectors in the RDD.

numCols - Number of elements in each Vector.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism)

seed - Random seed (default: a random long integer).

Returns:

RDD[Vector] with vectors containing i.i.d. samples ~ Exp(mean).

gammaJavaVectorRDD

public static JavaRDD<Vector> gammaJavaVectorRDD(JavaSparkContext jsc,
                                                 double shape,
                                                 double scale,
                                                 long numRows,
                                                 int numCols,
                                                 int numPartitions,
                                                 long seed)

Java-friendly version of RandomRDDs.gammaVectorRDD.

Parameters:: jsc - (undocumented); shape - (undocumented); scale - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

gammaJavaVectorRDD

public static JavaRDD<Vector> gammaJavaVectorRDD(JavaSparkContext jsc,
                                                 double shape,
                                                 double scale,
                                                 long numRows,
                                                 int numCols,
                                                 int numPartitions)

RandomRDDs.gammaJavaVectorRDD with the default seed.

Parameters:: jsc - (undocumented); shape - (undocumented); scale - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

gammaJavaVectorRDD

public static JavaRDD<Vector> gammaJavaVectorRDD(JavaSparkContext jsc,
                                                 double shape,
                                                 double scale,
                                                 long numRows,
                                                 int numCols)

RandomRDDs.gammaJavaVectorRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); shape - (undocumented); scale - (undocumented); numRows - (undocumented); numCols - (undocumented)
Returns:: (undocumented)

randomVectorRDD
```
public static RDD<Vector> randomVectorRDD(SparkContext sc,
                                          RandomDataGenerator<Object> generator,
                                          long numRows,
                                          int numCols,
                                          int numPartitions,
                                          long seed)
```
:: DeveloperApi :: Generates an RDD[Vector] with vectors containing i.i.d. samples produced by the input RandomDataGenerator.

Parameters:

sc - SparkContext used to create the RDD.

generator - RandomDataGenerator used to populate the RDD.

numRows - Number of Vectors in the RDD.

numCols - Number of elements in each Vector.

numPartitions - Number of partitions in the RDD (default: sc.defaultParallelism).

seed - Random seed (default: a random long integer).

Returns:

RDD[Vector] with vectors containing i.i.d. samples produced by generator.

randomJavaVectorRDD

public static JavaRDD<Vector> randomJavaVectorRDD(JavaSparkContext jsc,
                                                  RandomDataGenerator<Object> generator,
                                                  long numRows,
                                                  int numCols,
                                                  int numPartitions,
                                                  long seed)

:: DeveloperApi :: Java-friendly version of RandomRDDs.randomVectorRDD.

Parameters:: jsc - (undocumented); generator - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented); seed - (undocumented)
Returns:: (undocumented)

randomJavaVectorRDD

public static JavaRDD<Vector> randomJavaVectorRDD(JavaSparkContext jsc,
                                                  RandomDataGenerator<Object> generator,
                                                  long numRows,
                                                  int numCols,
                                                  int numPartitions)

:: DeveloperApi :: RandomRDDs.randomJavaVectorRDD with the default seed.

Parameters:: jsc - (undocumented); generator - (undocumented); numRows - (undocumented); numCols - (undocumented); numPartitions - (undocumented)
Returns:: (undocumented)

randomJavaVectorRDD

public static JavaRDD<Vector> randomJavaVectorRDD(JavaSparkContext jsc,
                                                  RandomDataGenerator<Object> generator,
                                                  long numRows,
                                                  int numCols)

:: DeveloperApi :: RandomRDDs.randomJavaVectorRDD with the default number of partitions and the default seed.

Parameters:: jsc - (undocumented); generator - (undocumented); numRows - (undocumented); numCols - (undocumented)
Returns:: (undocumented)

Class RandomRDDs

Constructor Summary

Method Summary

Methods inherited from class Object

Constructor Detail

RandomRDDs

Method Detail

uniformRDD

uniformJavaRDD

uniformJavaRDD

uniformJavaRDD

normalRDD

normalJavaRDD

normalJavaRDD

normalJavaRDD

poissonRDD

poissonJavaRDD

poissonJavaRDD

poissonJavaRDD

exponentialRDD

exponentialJavaRDD

exponentialJavaRDD

exponentialJavaRDD

gammaRDD

gammaJavaRDD

gammaJavaRDD

gammaJavaRDD

logNormalRDD

logNormalJavaRDD

logNormalJavaRDD

logNormalJavaRDD

randomRDD

randomJavaRDD

randomJavaRDD

randomJavaRDD

uniformVectorRDD

uniformJavaVectorRDD

uniformJavaVectorRDD

uniformJavaVectorRDD

normalVectorRDD

normalJavaVectorRDD

normalJavaVectorRDD

normalJavaVectorRDD

logNormalVectorRDD

logNormalJavaVectorRDD

logNormalJavaVectorRDD

logNormalJavaVectorRDD

poissonVectorRDD

poissonJavaVectorRDD

poissonJavaVectorRDD

poissonJavaVectorRDD

exponentialVectorRDD

exponentialJavaVectorRDD

exponentialJavaVectorRDD

exponentialJavaVectorRDD

gammaVectorRDD

gammaJavaVectorRDD

gammaJavaVectorRDD

gammaJavaVectorRDD

randomVectorRDD

randomJavaVectorRDD

randomJavaVectorRDD

randomJavaVectorRDD