org.apache.spark.mllib.random

RandomRDDs

object RandomRDDs

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

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

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

  16. def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD

    RandomRDDs#normalJavaRDD with the default seed.

  17. def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Java-friendly version of RandomRDDs#normalRDD.

  18. def normalJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int): JavaRDD[Vector]

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

  19. def normalJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    RandomRDDs#normalJavaVectorRDD with the default seed.

  20. def normalJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Java-friendly version of RandomRDDs#normalVectorRDD.

  21. def normalRDD(sc: SparkContext, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Generates an RDD comprised of i.

    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).

  22. 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.

    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).

  23. final def notify(): Unit

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

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

  26. def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Int): JavaDoubleRDD

    RandomRDDs#poissonJavaRDD with the default seed.

  27. def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Java-friendly version of RandomRDDs#poissonRDD.

  28. def poissonJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int): JavaRDD[Vector]

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

  29. def poissonJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    RandomRDDs#poissonJavaVectorRDD with the default seed.

  30. def poissonJavaVectorRDD(jsc: JavaSparkContext, mean: Double, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Java-friendly version of RandomRDDs#poissonVectorRDD.

  31. def poissonRDD(sc: SparkContext, mean: Double, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Generates an RDD comprised of i.

    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).

  32. 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.

    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).

  33. 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.

    :: 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[Double] comprised of i.i.d. samples produced by generator.

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  34. 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.

    :: 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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  35. final def synchronized[T0](arg0: ⇒ T0): T0

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

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

  38. def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD

    RandomRDDs#uniformJavaRDD with the default seed.

  39. def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int, seed: Long): JavaDoubleRDD

    Java-friendly version of RandomRDDs#uniformRDD.

  40. def uniformJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int): JavaRDD[Vector]

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

  41. def uniformJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]

    RandomRDDs#uniformJavaVectorRDD with the default seed.

  42. def uniformJavaVectorRDD(jsc: JavaSparkContext, numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]

    Java-friendly version of RandomRDDs#uniformVectorRDD.

  43. def uniformRDD(sc: SparkContext, size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong()): RDD[Double]

    Generates an RDD comprised of i.

    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).

  44. 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.

    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).

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  46. final def wait(arg0: Long, arg1: Int): Unit

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