object RandomRDDs
Generator methods for creating RDDs comprised of i.i.d.
samples from some distribution.
- Annotations
- @Since( "1.1.0" )
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- RandomRDDs.scala
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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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
def
finalize(): Unit
- Attributes
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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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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getClass(): Class[_]
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isInstanceOf[T0]: Boolean
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-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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 deviationGenerates 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" )
-
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" )
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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)
, useRandomRDDs.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" )
-
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" )
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
def
randomJavaRDD[T](jsc: JavaSparkContext, generator: RandomDataGenerator[T], size: Long): JavaRDD[T]
RandomRDDs.randomJavaRDD
with the default seed & numPartitionsRandomRDDs.randomJavaRDD
with the default seed & numPartitions- Annotations
- @Since( "1.6.0" )
-
def
randomJavaRDD[T](jsc: JavaSparkContext, generator: RandomDataGenerator[T], size: Long, numPartitions: Int): JavaRDD[T]
RandomRDDs.randomJavaRDD
with the default seed.RandomRDDs.randomJavaRDD
with the default seed.- Annotations
- @Since( "1.6.0" )
-
def
randomJavaRDD[T](jsc: JavaSparkContext, generator: RandomDataGenerator[T], size: Long, numPartitions: Int, seed: Long): JavaRDD[T]
Generates an RDD comprised of
i.i.d.
samples produced by the input RandomDataGenerator.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
- @Since( "1.6.0" )
-
def
randomJavaVectorRDD(jsc: JavaSparkContext, generator: RandomDataGenerator[Double], numRows: Long, numCols: Int): JavaRDD[Vector]
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
- @Since( "1.6.0" )
-
def
randomJavaVectorRDD(jsc: JavaSparkContext, generator: RandomDataGenerator[Double], numRows: Long, numCols: Int, numPartitions: Int): JavaRDD[Vector]
::
RandomRDDs.randomJavaVectorRDD
with the default seed.::
RandomRDDs.randomJavaVectorRDD
with the default seed.- Annotations
- @Since( "1.6.0" )
-
def
randomJavaVectorRDD(jsc: JavaSparkContext, generator: RandomDataGenerator[Double], numRows: Long, numCols: Int, numPartitions: Int, seed: Long): JavaRDD[Vector]
Java-friendly version of
RandomRDDs.randomVectorRDD
.Java-friendly version of
RandomRDDs.randomVectorRDD
.- Annotations
- @Since( "1.6.0" )
-
def
randomRDD[T](sc: SparkContext, generator: RandomDataGenerator[T], size: Long, numPartitions: Int = 0, seed: Long = Utils.random.nextLong())(implicit arg0: ClassTag[T]): RDD[T]
Generates an RDD comprised of
i.i.d.
samples produced by the input RandomDataGenerator.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
- @Since( "1.1.0" )
-
def
randomVectorRDD(sc: SparkContext, generator: RandomDataGenerator[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 produced by the input RandomDataGenerator.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
- @Since( "1.1.0" )
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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" )
-
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 distributionU(0.0, 1.0)
.Generates an RDD comprised of
i.i.d.
samples from the uniform distributionU(0.0, 1.0)
.To transform the distribution in the generated RDD from
U(0.0, 1.0)
toU(a, b)
, useRandomRDDs.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" )
-
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 onU(0.0, 1.0)
.Generates an RDD[Vector] with vectors containing
i.i.d.
samples drawn from the uniform distribution onU(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" )
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()