Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package random
    Definition Classes
    mllib
  • ExponentialGenerator
  • GammaGenerator
  • LogNormalGenerator
  • PoissonGenerator
  • RandomDataGenerator
  • RandomRDDs
  • StandardNormalGenerator
  • UniformGenerator
  • WeibullGenerator

object RandomRDDs

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

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  8. def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD

    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

    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

    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]

    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]

    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]

    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]

    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]

    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
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  17. def gammaJavaRDD(jsc: JavaSparkContext, shape: Double, scale: Double, size: Long): JavaDoubleRDD

    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

    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

    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]

    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]

    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]

    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]

    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 (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).

    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]

    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 (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).

    Annotations
    @Since( "1.3.0" )
  25. final def getClass(): Class[_]
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  28. def logNormalJavaRDD(jsc: JavaSparkContext, mean: Double, std: Double, size: Long): JavaDoubleRDD

    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

    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

    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]

    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]

    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]

    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]

    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]

    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
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  37. def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD

    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

    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

    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]

    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]

    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]

    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]

    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]

    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
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  47. def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD

    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

    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

    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]

    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]

    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]

    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]

    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]

    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]

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

    :: DeveloperApi :: 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]

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

    :: DeveloperApi :: 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]

    :: 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]

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

    :: DeveloperApi :: 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]

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

    :: DeveloperApi :: 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]

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

    :: DeveloperApi :: 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]

    :: 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]

    :: 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.

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    @DeveloperApi() @Since( "1.1.0" )
  63. final def synchronized[T0](arg0: ⇒ T0): T0
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  64. def toString(): String
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  65. def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD

    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

    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

    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]

    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]

    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]

    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]

    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]

    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
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    @throws( ... )
  74. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  75. final def wait(arg0: Long): Unit
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    @native() @throws( ... )

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