Object

org.apache.spark.mllib.random

RandomRDDs

Related Doc: package random

Permalink

object RandomRDDs

Generator methods for creating RDDs comprised of i.i.d. samples from some distribution.

Annotations
@Since( "1.1.0" )
Source
RandomRDDs.scala
Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. RandomRDDs
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  5. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  8. def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD

    Permalink

    RandomRDDs#exponentialJavaRDD with the default number of partitions and the default seed.

    RandomRDDs#exponentialJavaRDD with the default number of partitions and the default seed.

    Annotations
    @Since( "1.3.0" )
  9. def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Int): JavaDoubleRDD

    Permalink

    RandomRDDs#exponentialJavaRDD with the default seed.

    RandomRDDs#exponentialJavaRDD with the default seed.

    Annotations
    @Since( "1.3.0" )
  10. def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Permalink

    Java-friendly version of RandomRDDs#exponentialRDD.

    Java-friendly version of RandomRDDs#exponentialRDD.

    Annotations
    @Since( "1.3.0" )
  11. def exponentialJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int): JavaRDD[Vector]

    Permalink

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

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

    Annotations
    @Since( "1.3.0" )
  12. def exponentialJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#exponentialJavaVectorRDD with the default seed.

    RandomRDDs#exponentialJavaVectorRDD with the default seed.

    Annotations
    @Since( "1.3.0" )
  13. def exponentialJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Permalink

    Java-friendly version of RandomRDDs#exponentialVectorRDD.

    Java-friendly version of RandomRDDs#exponentialVectorRDD.

    Annotations
    @Since( "1.3.0" )
  14. def exponentialRDD(sc: SparkContext, mean: Double, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Permalink

    Generates an RDD comprised of i.i.d. samples from the exponential distribution with the input mean.

    Generates an RDD comprised of i.i.d. samples from the exponential distribution with the input mean.

    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).

    Annotations
    @Since( "1.3.0" )
  15. def exponentialVectorRDD(sc: SparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Vector]

    Permalink

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the exponential distribution with the input mean.

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the exponential distribution with the input mean.

    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).

    Annotations
    @Since( "1.3.0" )
  16. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  17. def gammaJavaRDD(jsc: JavaSparkContext, shape: Double, scale: Double, size: Long): JavaDoubleRDD

    Permalink

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

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

    Annotations
    @Since( "1.3.0" )
  18. def gammaJavaRDD(jsc: JavaSparkContext, shape: Double, scale: Double, size: Long, numPartitions: Int): JavaDoubleRDD

    Permalink

    RandomRDDs#gammaJavaRDD with the default seed.

    RandomRDDs#gammaJavaRDD with the default seed.

    Annotations
    @Since( "1.3.0" )
  19. def gammaJavaRDD(jsc: JavaSparkContext, shape: Double, scale: Double, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Permalink

    Java-friendly version of RandomRDDs#gammaRDD.

    Java-friendly version of RandomRDDs#gammaRDD.

    Annotations
    @Since( "1.3.0" )
  20. def gammaJavaVectorRDD(jsc: JavaSparkContext, shape: Double, scale: Double, numRows: Long, numCols: Int): JavaRDD[Vector]

    Permalink

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

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

    Annotations
    @Since( "1.3.0" )
  21. def gammaJavaVectorRDD(jsc: JavaSparkContext, shape: Double, scale: Double, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#gammaJavaVectorRDD with the default seed.

    RandomRDDs#gammaJavaVectorRDD with the default seed.

    Annotations
    @Since( "1.3.0" )
  22. def gammaJavaVectorRDD(jsc: JavaSparkContext, shape: Double, scale: Double, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Permalink

    Java-friendly version of RandomRDDs#gammaVectorRDD.

    Java-friendly version of RandomRDDs#gammaVectorRDD.

    Annotations
    @Since( "1.3.0" )
  23. def gammaRDD(sc: SparkContext, shape: Double, scale: Double, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Permalink

    Generates an RDD comprised of i.i.d. samples from the gamma distribution with the input shape and scale.

    Generates an RDD comprised of i.i.d. samples from the gamma distribution with the input shape and scale.

    sc

    SparkContext used to create the RDD.

    shape

    shape parameter (> 0) for the gamma distribution

    scale

    scale parameter (> 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).

    Annotations
    @Since( "1.3.0" )
  24. def gammaVectorRDD(sc: SparkContext, shape: Double, scale: Double, numRows: Long, numCols: Int, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Vector]

    Permalink

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the gamma distribution with the input shape and scale.

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the gamma distribution with the input shape and scale.

    sc

    SparkContext used to create the RDD.

    shape

    shape parameter (> 0) for the gamma distribution.

    scale

    scale parameter (> 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).

    Annotations
    @Since( "1.3.0" )
  25. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  26. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  27. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  28. def logNormalJavaRDD(jsc: JavaSparkContext, mean: Double, std: Double, size: Long): JavaDoubleRDD

    Permalink

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

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

    Annotations
    @Since( "1.3.0" )
  29. def logNormalJavaRDD(jsc: JavaSparkContext, mean: Double, std: Double, size: Long, numPartitions: Int): JavaDoubleRDD

    Permalink

    RandomRDDs#logNormalJavaRDD with the default seed.

    RandomRDDs#logNormalJavaRDD with the default seed.

    Annotations
    @Since( "1.3.0" )
  30. def logNormalJavaRDD(jsc: JavaSparkContext, mean: Double, std: Double, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Permalink

    Java-friendly version of RandomRDDs#logNormalRDD.

    Java-friendly version of RandomRDDs#logNormalRDD.

    Annotations
    @Since( "1.3.0" )
  31. def logNormalJavaVectorRDD(jsc: JavaSparkContext, mean: Double, std: Double, numRows: Long, numCols: Int): JavaRDD[Vector]

    Permalink

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

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

    Annotations
    @Since( "1.3.0" )
  32. def logNormalJavaVectorRDD(jsc: JavaSparkContext, mean: Double, std: Double, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#logNormalJavaVectorRDD with the default seed.

    RandomRDDs#logNormalJavaVectorRDD with the default seed.

    Annotations
    @Since( "1.3.0" )
  33. def logNormalJavaVectorRDD(jsc: JavaSparkContext, mean: Double, std: Double, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Permalink

    Java-friendly version of RandomRDDs#logNormalVectorRDD.

    Java-friendly version of RandomRDDs#logNormalVectorRDD.

    Annotations
    @Since( "1.3.0" )
  34. def logNormalRDD(sc: SparkContext, mean: Double, std: Double, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Permalink

    Generates an RDD comprised of i.i.d. samples from the log normal distribution with the input mean and standard deviation

    Generates an RDD comprised of i.i.d. samples from the log normal distribution with the input mean and standard deviation

    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).

    Annotations
    @Since( "1.3.0" )
  35. def logNormalVectorRDD(sc: SparkContext, mean: Double, std: Double, numRows: Long, numCols: Int, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Vector]

    Permalink

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from a log normal distribution.

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from a log normal distribution.

    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.

    Annotations
    @Since( "1.3.0" )
  36. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  37. def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD

    Permalink

    RandomRDDs#normalJavaRDD with the default number of partitions and the default seed.

    RandomRDDs#normalJavaRDD with the default number of partitions and the default seed.

    Annotations
    @Since( "1.1.0" )
  38. def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD

    Permalink

    RandomRDDs#normalJavaRDD with the default seed.

    RandomRDDs#normalJavaRDD with the default seed.

    Annotations
    @Since( "1.1.0" )
  39. def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Permalink

    Java-friendly version of RandomRDDs#normalRDD.

    Java-friendly version of RandomRDDs#normalRDD.

    Annotations
    @Since( "1.1.0" )
  40. def normalJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int): JavaRDD[Vector]

    Permalink

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

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

    Annotations
    @Since( "1.1.0" )
  41. def normalJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#normalJavaVectorRDD with the default seed.

    RandomRDDs#normalJavaVectorRDD with the default seed.

    Annotations
    @Since( "1.1.0" )
  42. def normalJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Permalink

    Java-friendly version of RandomRDDs#normalVectorRDD.

    Java-friendly version of RandomRDDs#normalVectorRDD.

    Annotations
    @Since( "1.1.0" )
  43. def normalRDD(sc: SparkContext, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Permalink

    Generates an RDD comprised of i.i.d. samples from the standard normal distribution.

    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, sigma2), use RandomRDDs.normalRDD(sc, n, p, seed).map(v => mean + sigma * v).

    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).

    Annotations
    @Since( "1.1.0" )
  44. def normalVectorRDD(sc: SparkContext, numRows: Long, numCols: Int, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Vector]

    Permalink

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the standard normal distribution.

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the standard normal distribution.

    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).

    Annotations
    @Since( "1.1.0" )
  45. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  46. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  47. def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD

    Permalink

    RandomRDDs#poissonJavaRDD with the default number of partitions and the default seed.

    RandomRDDs#poissonJavaRDD with the default number of partitions and the default seed.

    Annotations
    @Since( "1.1.0" )
  48. def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Int): JavaDoubleRDD

    Permalink

    RandomRDDs#poissonJavaRDD with the default seed.

    RandomRDDs#poissonJavaRDD with the default seed.

    Annotations
    @Since( "1.1.0" )
  49. def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Permalink

    Java-friendly version of RandomRDDs#poissonRDD.

    Java-friendly version of RandomRDDs#poissonRDD.

    Annotations
    @Since( "1.1.0" )
  50. def poissonJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int): JavaRDD[Vector]

    Permalink

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

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

    Annotations
    @Since( "1.1.0" )
  51. def poissonJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#poissonJavaVectorRDD with the default seed.

    RandomRDDs#poissonJavaVectorRDD with the default seed.

    Annotations
    @Since( "1.1.0" )
  52. def poissonJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Permalink

    Java-friendly version of RandomRDDs#poissonVectorRDD.

    Java-friendly version of RandomRDDs#poissonVectorRDD.

    Annotations
    @Since( "1.1.0" )
  53. def poissonRDD(sc: SparkContext, mean: Double, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Permalink

    Generates an RDD comprised of i.i.d. samples from the Poisson distribution with the input mean.

    Generates an RDD comprised of i.i.d. samples from the Poisson distribution with the input mean.

    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).

    Annotations
    @Since( "1.1.0" )
  54. def poissonVectorRDD(sc: SparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Vector]

    Permalink

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the Poisson distribution with the input mean.

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the Poisson distribution with the input mean.

    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).

    Annotations
    @Since( "1.1.0" )
  55. def randomJavaRDD[T](jsc: JavaSparkContext, generator: RandomDataGenerator[T], size: Long): JavaRDD[T]

    Permalink

    RandomRDDs#randomJavaRDD with the default seed & numPartitions

    RandomRDDs#randomJavaRDD with the default seed & numPartitions

    Annotations
    @DeveloperApi() @Since( "1.6.0" )
  56. def randomJavaRDD[T](jsc: JavaSparkContext, generator: RandomDataGenerator[T], size: Long, numPartitions: Int): JavaRDD[T]

    Permalink

    RandomRDDs#randomJavaRDD with the default seed.

    RandomRDDs#randomJavaRDD with the default seed.

    Annotations
    @DeveloperApi() @Since( "1.6.0" )
  57. def randomJavaRDD[T](jsc: JavaSparkContext, generator: RandomDataGenerator[T], size: Long, numPartitions: Int, seed: Long): JavaRDD[T]

    Permalink

    :: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.

    :: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.

    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.

    Annotations
    @DeveloperApi() @Since( "1.6.0" )
  58. def randomJavaVectorRDD(jsc: JavaSparkContext, generator: RandomDataGenerator[Double], numRows: Long, numCols: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#randomJavaVectorRDD with the default number of partitions and the default seed.

    RandomRDDs#randomJavaVectorRDD with the default number of partitions and the default seed.

    Annotations
    @DeveloperApi() @Since( "1.6.0" )
  59. def randomJavaVectorRDD(jsc: JavaSparkContext, generator: RandomDataGenerator[Double], numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#randomJavaVectorRDD with the default seed.

    RandomRDDs#randomJavaVectorRDD with the default seed.

    Annotations
    @DeveloperApi() @Since( "1.6.0" )
  60. def randomJavaVectorRDD(jsc: JavaSparkContext, generator: RandomDataGenerator[Double], numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Permalink

    Java-friendly version of RandomRDDs#randomVectorRDD.

    Java-friendly version of RandomRDDs#randomVectorRDD.

    Annotations
    @DeveloperApi() @Since( "1.6.0" )
  61. def randomRDD[T](sc: SparkContext, generator: RandomDataGenerator[T], size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong())(implicit arg0: ClassTag[T]): RDD[T]

    Permalink

    :: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.

    :: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.

    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).

    returns

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

    Annotations
    @DeveloperApi() @Since( "1.1.0" )
  62. def randomVectorRDD(sc: SparkContext, generator: RandomDataGenerator[Double], numRows: Long, numCols: Int, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Vector]

    Permalink

    :: DeveloperApi :: Generates an RDD[Vector] with vectors containing i.i.d. samples produced by the input RandomDataGenerator.

    :: DeveloperApi :: Generates an RDD[Vector] with vectors containing i.i.d. samples produced by the input RandomDataGenerator.

    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.

    Annotations
    @DeveloperApi() @Since( "1.1.0" )
  63. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  64. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  65. def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD

    Permalink

    RandomRDDs#uniformJavaRDD with the default number of partitions and the default seed.

    RandomRDDs#uniformJavaRDD with the default number of partitions and the default seed.

    Annotations
    @Since( "1.1.0" )
  66. def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD

    Permalink

    RandomRDDs#uniformJavaRDD with the default seed.

    RandomRDDs#uniformJavaRDD with the default seed.

    Annotations
    @Since( "1.1.0" )
  67. def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Permalink

    Java-friendly version of RandomRDDs#uniformRDD.

    Java-friendly version of RandomRDDs#uniformRDD.

    Annotations
    @Since( "1.1.0" )
  68. def uniformJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int): JavaRDD[Vector]

    Permalink

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

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

    Annotations
    @Since( "1.1.0" )
  69. def uniformJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    Permalink

    RandomRDDs#uniformJavaVectorRDD with the default seed.

    RandomRDDs#uniformJavaVectorRDD with the default seed.

    Annotations
    @Since( "1.1.0" )
  70. def uniformJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Permalink

    Java-friendly version of RandomRDDs#uniformVectorRDD.

    Java-friendly version of RandomRDDs#uniformVectorRDD.

    Annotations
    @Since( "1.1.0" )
  71. def uniformRDD(sc: SparkContext, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Permalink

    Generates an RDD comprised of i.i.d. samples from the uniform distribution U(0.0, 1.0).

    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).

    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).

    Annotations
    @Since( "1.1.0" )
  72. def uniformVectorRDD(sc: SparkContext, numRows: Long, numCols: Int, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Vector]

    Permalink

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the uniform distribution on U(0.0, 1.0).

    Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the uniform distribution on U(0.0, 1.0).

    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).

    Annotations
    @Since( "1.1.0" )
  73. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  74. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  75. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped