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RandomRDDs.scala
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RandomRDDs.scala
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.mllib.random
import scala.reflect.ClassTag
import org.apache.spark.SparkContext
import org.apache.spark.annotation.Since
import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD, JavaSparkContext}
import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.rdd.{RandomRDD, RandomVectorRDD}
import org.apache.spark.rdd.RDD
import org.apache.spark.util.Utils
/**
* Generator methods for creating RDDs comprised of `i.i.d.` samples from some distribution.
*/
@Since("1.1.0")
object RandomRDDs {
/**
* 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)`.
*
* @param sc SparkContext used to create the RDD.
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Double] comprised of `i.i.d.` samples ~ `U(0.0, 1.0)`.
*/
@Since("1.1.0")
def uniformRDD(
sc: SparkContext,
size: Long,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Double] = {
val uniform = new UniformGenerator()
randomRDD(sc, uniform, size, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.uniformRDD`.
*/
@Since("1.1.0")
def uniformJavaRDD(
jsc: JavaSparkContext,
size: Long,
numPartitions: Int,
seed: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size, numPartitions, seed))
}
/**
* `RandomRDDs.uniformJavaRDD` with the default seed.
*/
@Since("1.1.0")
def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size, numPartitions))
}
/**
* `RandomRDDs.uniformJavaRDD` with the default number of partitions and the default seed.
*/
@Since("1.1.0")
def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size))
}
/**
* 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)`.
*
* @param sc SparkContext used to create the RDD.
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Double] comprised of `i.i.d.` samples ~ N(0.0, 1.0).
*/
@Since("1.1.0")
def normalRDD(
sc: SparkContext,
size: Long,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Double] = {
val normal = new StandardNormalGenerator()
randomRDD(sc, normal, size, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.normalRDD`.
*/
@Since("1.1.0")
def normalJavaRDD(
jsc: JavaSparkContext,
size: Long,
numPartitions: Int,
seed: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size, numPartitions, seed))
}
/**
* `RandomRDDs.normalJavaRDD` with the default seed.
*/
@Since("1.1.0")
def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size, numPartitions))
}
/**
* `RandomRDDs.normalJavaRDD` with the default number of partitions and the default seed.
*/
@Since("1.1.0")
def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size))
}
/**
* Generates an RDD comprised of `i.i.d.` samples from the Poisson distribution with the input
* mean.
*
* @param sc SparkContext used to create the RDD.
* @param mean Mean, or lambda, for the Poisson distribution.
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
*/
@Since("1.1.0")
def poissonRDD(
sc: SparkContext,
mean: Double,
size: Long,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Double] = {
val poisson = new PoissonGenerator(mean)
randomRDD(sc, poisson, size, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.poissonRDD`.
*/
@Since("1.1.0")
def poissonJavaRDD(
jsc: JavaSparkContext,
mean: Double,
size: Long,
numPartitions: Int,
seed: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size, numPartitions, seed))
}
/**
* `RandomRDDs.poissonJavaRDD` with the default seed.
*/
@Since("1.1.0")
def poissonJavaRDD(
jsc: JavaSparkContext,
mean: Double,
size: Long,
numPartitions: Int): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size, numPartitions))
}
/**
* `RandomRDDs.poissonJavaRDD` with the default number of partitions and the default seed.
*/
@Since("1.1.0")
def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size))
}
/**
* Generates an RDD comprised of `i.i.d.` samples from the exponential distribution with
* the input mean.
*
* @param sc SparkContext used to create the RDD.
* @param mean Mean, or 1 / lambda, for the exponential distribution.
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
*/
@Since("1.3.0")
def exponentialRDD(
sc: SparkContext,
mean: Double,
size: Long,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Double] = {
val exponential = new ExponentialGenerator(mean)
randomRDD(sc, exponential, size, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.exponentialRDD`.
*/
@Since("1.3.0")
def exponentialJavaRDD(
jsc: JavaSparkContext,
mean: Double,
size: Long,
numPartitions: Int,
seed: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(exponentialRDD(jsc.sc, mean, size, numPartitions, seed))
}
/**
* `RandomRDDs.exponentialJavaRDD` with the default seed.
*/
@Since("1.3.0")
def exponentialJavaRDD(
jsc: JavaSparkContext,
mean: Double,
size: Long,
numPartitions: Int): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(exponentialRDD(jsc.sc, mean, size, numPartitions))
}
/**
* `RandomRDDs.exponentialJavaRDD` with the default number of partitions and the default seed.
*/
@Since("1.3.0")
def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(exponentialRDD(jsc.sc, mean, size))
}
/**
* Generates an RDD comprised of `i.i.d.` samples from the gamma distribution with the input
* shape and scale.
*
* @param sc SparkContext used to create the RDD.
* @param shape shape parameter (greater than 0) for the gamma distribution
* @param scale scale parameter (greater than 0) for the gamma distribution
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
*/
@Since("1.3.0")
def gammaRDD(
sc: SparkContext,
shape: Double,
scale: Double,
size: Long,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Double] = {
val gamma = new GammaGenerator(shape, scale)
randomRDD(sc, gamma, size, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.gammaRDD`.
*/
@Since("1.3.0")
def gammaJavaRDD(
jsc: JavaSparkContext,
shape: Double,
scale: Double,
size: Long,
numPartitions: Int,
seed: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(gammaRDD(jsc.sc, shape, scale, size, numPartitions, seed))
}
/**
* `RandomRDDs.gammaJavaRDD` with the default seed.
*/
@Since("1.3.0")
def gammaJavaRDD(
jsc: JavaSparkContext,
shape: Double,
scale: Double,
size: Long,
numPartitions: Int): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(gammaRDD(jsc.sc, shape, scale, size, numPartitions))
}
/**
* `RandomRDDs.gammaJavaRDD` with the default number of partitions and the default seed.
*/
@Since("1.3.0")
def gammaJavaRDD(
jsc: JavaSparkContext,
shape: Double,
scale: Double,
size: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(gammaRDD(jsc.sc, shape, scale, size))
}
/**
* Generates an RDD comprised of `i.i.d.` samples from the log normal distribution with the input
* mean and standard deviation
*
* @param sc SparkContext used to create the RDD.
* @param mean mean for the log normal distribution
* @param std standard deviation for the log normal distribution
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
*/
@Since("1.3.0")
def logNormalRDD(
sc: SparkContext,
mean: Double,
std: Double,
size: Long,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Double] = {
val logNormal = new LogNormalGenerator(mean, std)
randomRDD(sc, logNormal, size, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.logNormalRDD`.
*/
@Since("1.3.0")
def logNormalJavaRDD(
jsc: JavaSparkContext,
mean: Double,
std: Double,
size: Long,
numPartitions: Int,
seed: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(logNormalRDD(jsc.sc, mean, std, size, numPartitions, seed))
}
/**
* `RandomRDDs.logNormalJavaRDD` with the default seed.
*/
@Since("1.3.0")
def logNormalJavaRDD(
jsc: JavaSparkContext,
mean: Double,
std: Double,
size: Long,
numPartitions: Int): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(logNormalRDD(jsc.sc, mean, std, size, numPartitions))
}
/**
* `RandomRDDs.logNormalJavaRDD` with the default number of partitions and the default seed.
*/
@Since("1.3.0")
def logNormalJavaRDD(
jsc: JavaSparkContext,
mean: Double,
std: Double,
size: Long): JavaDoubleRDD = {
JavaDoubleRDD.fromRDD(logNormalRDD(jsc.sc, mean, std, size))
}
/**
* Generates an RDD comprised of `i.i.d.` samples produced by the input RandomDataGenerator.
*
* @param sc SparkContext used to create the RDD.
* @param generator RandomDataGenerator used to populate the RDD.
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[T] comprised of `i.i.d.` samples produced by generator.
*/
@Since("1.1.0")
def randomRDD[T: ClassTag](
sc: SparkContext,
generator: RandomDataGenerator[T],
size: Long,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[T] = {
new RandomRDD[T](sc, size, numPartitionsOrDefault(sc, numPartitions), generator, seed)
}
/**
* Generates an RDD comprised of `i.i.d.` samples produced by the input RandomDataGenerator.
*
* @param jsc JavaSparkContext used to create the RDD.
* @param generator RandomDataGenerator used to populate the RDD.
* @param size Size of the RDD.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[T] comprised of `i.i.d.` samples produced by generator.
*/
@Since("1.6.0")
def randomJavaRDD[T](
jsc: JavaSparkContext,
generator: RandomDataGenerator[T],
size: Long,
numPartitions: Int,
seed: Long): JavaRDD[T] = {
implicit val ctag: ClassTag[T] = fakeClassTag
val rdd = randomRDD(jsc.sc, generator, size, numPartitions, seed)
JavaRDD.fromRDD(rdd)
}
/**
* `RandomRDDs.randomJavaRDD` with the default seed.
*/
@Since("1.6.0")
def randomJavaRDD[T](
jsc: JavaSparkContext,
generator: RandomDataGenerator[T],
size: Long,
numPartitions: Int): JavaRDD[T] = {
randomJavaRDD(jsc, generator, size, numPartitions, Utils.random.nextLong())
}
/**
* `RandomRDDs.randomJavaRDD` with the default seed & numPartitions
*/
@Since("1.6.0")
def randomJavaRDD[T](
jsc: JavaSparkContext,
generator: RandomDataGenerator[T],
size: Long): JavaRDD[T] = {
randomJavaRDD(jsc, generator, size, 0)
}
// TODO Generate RDD[Vector] from multivariate distributions.
/**
* Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the
* uniform distribution on `U(0.0, 1.0)`.
*
* @param sc SparkContext used to create the RDD.
* @param numRows Number of Vectors in the RDD.
* @param numCols Number of elements in each Vector.
* @param numPartitions Number of partitions in the RDD.
* @param seed Seed for the RNG that generates the seed for the generator in each partition.
* @return RDD[Vector] with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
*/
@Since("1.1.0")
def uniformVectorRDD(
sc: SparkContext,
numRows: Long,
numCols: Int,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Vector] = {
val uniform = new UniformGenerator()
randomVectorRDD(sc, uniform, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.uniformVectorRDD`.
*/
@Since("1.1.0")
def uniformJavaVectorRDD(
jsc: JavaSparkContext,
numRows: Long,
numCols: Int,
numPartitions: Int,
seed: Long): JavaRDD[Vector] = {
uniformVectorRDD(jsc.sc, numRows, numCols, numPartitions, seed).toJavaRDD()
}
/**
* `RandomRDDs.uniformJavaVectorRDD` with the default seed.
*/
@Since("1.1.0")
def uniformJavaVectorRDD(
jsc: JavaSparkContext,
numRows: Long,
numCols: Int,
numPartitions: Int): JavaRDD[Vector] = {
uniformVectorRDD(jsc.sc, numRows, numCols, numPartitions).toJavaRDD()
}
/**
* `RandomRDDs.uniformJavaVectorRDD` with the default number of partitions and the default seed.
*/
@Since("1.1.0")
def uniformJavaVectorRDD(
jsc: JavaSparkContext,
numRows: Long,
numCols: Int): JavaRDD[Vector] = {
uniformVectorRDD(jsc.sc, numRows, numCols).toJavaRDD()
}
/**
* Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the
* standard normal distribution.
*
* @param sc SparkContext used to create the RDD.
* @param numRows Number of Vectors in the RDD.
* @param numCols Number of elements in each Vector.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Vector] with vectors containing `i.i.d.` samples ~ `N(0.0, 1.0)`.
*/
@Since("1.1.0")
def normalVectorRDD(
sc: SparkContext,
numRows: Long,
numCols: Int,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Vector] = {
val normal = new StandardNormalGenerator()
randomVectorRDD(sc, normal, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.normalVectorRDD`.
*/
@Since("1.1.0")
def normalJavaVectorRDD(
jsc: JavaSparkContext,
numRows: Long,
numCols: Int,
numPartitions: Int,
seed: Long): JavaRDD[Vector] = {
normalVectorRDD(jsc.sc, numRows, numCols, numPartitions, seed).toJavaRDD()
}
/**
* `RandomRDDs.normalJavaVectorRDD` with the default seed.
*/
@Since("1.1.0")
def normalJavaVectorRDD(
jsc: JavaSparkContext,
numRows: Long,
numCols: Int,
numPartitions: Int): JavaRDD[Vector] = {
normalVectorRDD(jsc.sc, numRows, numCols, numPartitions).toJavaRDD()
}
/**
* `RandomRDDs.normalJavaVectorRDD` with the default number of partitions and the default seed.
*/
@Since("1.1.0")
def normalJavaVectorRDD(
jsc: JavaSparkContext,
numRows: Long,
numCols: Int): JavaRDD[Vector] = {
normalVectorRDD(jsc.sc, numRows, numCols).toJavaRDD()
}
/**
* Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from a
* log normal distribution.
*
* @param sc SparkContext used to create the RDD.
* @param mean Mean of the log normal distribution.
* @param std Standard deviation of the log normal distribution.
* @param numRows Number of Vectors in the RDD.
* @param numCols Number of elements in each Vector.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Vector] with vectors containing `i.i.d.` samples.
*/
@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] = {
val logNormal = new LogNormalGenerator(mean, std)
randomVectorRDD(sc, logNormal, numRows, numCols,
numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.logNormalVectorRDD`.
*/
@Since("1.3.0")
def logNormalJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
std: Double,
numRows: Long,
numCols: Int,
numPartitions: Int,
seed: Long): JavaRDD[Vector] = {
logNormalVectorRDD(jsc.sc, mean, std, numRows, numCols, numPartitions, seed).toJavaRDD()
}
/**
* `RandomRDDs.logNormalJavaVectorRDD` with the default seed.
*/
@Since("1.3.0")
def logNormalJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
std: Double,
numRows: Long,
numCols: Int,
numPartitions: Int): JavaRDD[Vector] = {
logNormalVectorRDD(jsc.sc, mean, std, numRows, numCols, numPartitions).toJavaRDD()
}
/**
* `RandomRDDs.logNormalJavaVectorRDD` with the default number of partitions and
* the default seed.
*/
@Since("1.3.0")
def logNormalJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
std: Double,
numRows: Long,
numCols: Int): JavaRDD[Vector] = {
logNormalVectorRDD(jsc.sc, mean, std, numRows, numCols).toJavaRDD()
}
/**
* Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the
* Poisson distribution with the input mean.
*
* @param sc SparkContext used to create the RDD.
* @param mean Mean, or lambda, for the Poisson distribution.
* @param numRows Number of Vectors in the RDD.
* @param numCols Number of elements in each Vector.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`)
* @param seed Random seed (default: a random long integer).
* @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Pois(mean).
*/
@Since("1.1.0")
def poissonVectorRDD(
sc: SparkContext,
mean: Double,
numRows: Long,
numCols: Int,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Vector] = {
val poisson = new PoissonGenerator(mean)
randomVectorRDD(sc, poisson, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.poissonVectorRDD`.
*/
@Since("1.1.0")
def poissonJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
numRows: Long,
numCols: Int,
numPartitions: Int,
seed: Long): JavaRDD[Vector] = {
poissonVectorRDD(jsc.sc, mean, numRows, numCols, numPartitions, seed).toJavaRDD()
}
/**
* `RandomRDDs.poissonJavaVectorRDD` with the default seed.
*/
@Since("1.1.0")
def poissonJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
numRows: Long,
numCols: Int,
numPartitions: Int): JavaRDD[Vector] = {
poissonVectorRDD(jsc.sc, mean, numRows, numCols, numPartitions).toJavaRDD()
}
/**
* `RandomRDDs.poissonJavaVectorRDD` with the default number of partitions and the default seed.
*/
@Since("1.1.0")
def poissonJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
numRows: Long,
numCols: Int): JavaRDD[Vector] = {
poissonVectorRDD(jsc.sc, mean, numRows, numCols).toJavaRDD()
}
/**
* Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the
* exponential distribution with the input mean.
*
* @param sc SparkContext used to create the RDD.
* @param mean Mean, or 1 / lambda, for the Exponential distribution.
* @param numRows Number of Vectors in the RDD.
* @param numCols Number of elements in each Vector.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`)
* @param seed Random seed (default: a random long integer).
* @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean).
*/
@Since("1.3.0")
def exponentialVectorRDD(
sc: SparkContext,
mean: Double,
numRows: Long,
numCols: Int,
numPartitions: Int = 0,
seed: Long = Utils.random.nextLong()): RDD[Vector] = {
val exponential = new ExponentialGenerator(mean)
randomVectorRDD(sc, exponential, numRows, numCols,
numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.exponentialVectorRDD`.
*/
@Since("1.3.0")
def exponentialJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
numRows: Long,
numCols: Int,
numPartitions: Int,
seed: Long): JavaRDD[Vector] = {
exponentialVectorRDD(jsc.sc, mean, numRows, numCols, numPartitions, seed).toJavaRDD()
}
/**
* `RandomRDDs.exponentialJavaVectorRDD` with the default seed.
*/
@Since("1.3.0")
def exponentialJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
numRows: Long,
numCols: Int,
numPartitions: Int): JavaRDD[Vector] = {
exponentialVectorRDD(jsc.sc, mean, numRows, numCols, numPartitions).toJavaRDD()
}
/**
* `RandomRDDs.exponentialJavaVectorRDD` with the default number of partitions
* and the default seed.
*/
@Since("1.3.0")
def exponentialJavaVectorRDD(
jsc: JavaSparkContext,
mean: Double,
numRows: Long,
numCols: Int): JavaRDD[Vector] = {
exponentialVectorRDD(jsc.sc, mean, numRows, numCols).toJavaRDD()
}
/**
* Generates an RDD[Vector] with vectors containing `i.i.d.` samples drawn from the
* gamma distribution with the input shape and scale.
*
* @param sc SparkContext used to create the RDD.
* @param shape shape parameter (greater than 0) for the gamma distribution.
* @param scale scale parameter (greater than 0) for the gamma distribution.
* @param numRows Number of Vectors in the RDD.
* @param numCols Number of elements in each Vector.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`)
* @param seed Random seed (default: a random long integer).
* @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean).
*/
@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] = {
val gamma = new GammaGenerator(shape, scale)
randomVectorRDD(sc, gamma, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), seed)
}
/**
* Java-friendly version of `RandomRDDs.gammaVectorRDD`.
*/
@Since("1.3.0")
def gammaJavaVectorRDD(
jsc: JavaSparkContext,
shape: Double,
scale: Double,
numRows: Long,
numCols: Int,
numPartitions: Int,
seed: Long): JavaRDD[Vector] = {
gammaVectorRDD(jsc.sc, shape, scale, numRows, numCols, numPartitions, seed).toJavaRDD()
}
/**
* `RandomRDDs.gammaJavaVectorRDD` with the default seed.
*/
@Since("1.3.0")
def gammaJavaVectorRDD(
jsc: JavaSparkContext,
shape: Double,
scale: Double,
numRows: Long,
numCols: Int,
numPartitions: Int): JavaRDD[Vector] = {
gammaVectorRDD(jsc.sc, shape, scale, numRows, numCols, numPartitions).toJavaRDD()
}
/**
* `RandomRDDs.gammaJavaVectorRDD` with the default number of partitions and the default seed.
*/
@Since("1.3.0")
def gammaJavaVectorRDD(
jsc: JavaSparkContext,
shape: Double,
scale: Double,
numRows: Long,
numCols: Int): JavaRDD[Vector] = {
gammaVectorRDD(jsc.sc, shape, scale, numRows, numCols).toJavaRDD()
}
/**
* Generates an RDD[Vector] with vectors containing `i.i.d.` samples produced by the
* input RandomDataGenerator.
*
* @param sc SparkContext used to create the RDD.
* @param generator RandomDataGenerator used to populate the RDD.
* @param numRows Number of Vectors in the RDD.
* @param numCols Number of elements in each Vector.
* @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
* @param seed Random seed (default: a random long integer).
* @return RDD[Vector] with vectors containing `i.i.d.` samples produced by generator.
*/
@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] = {
new RandomVectorRDD(
sc, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), generator, seed)
}
/**
* Java-friendly version of `RandomRDDs.randomVectorRDD`.
*/
@Since("1.6.0")
def randomJavaVectorRDD(
jsc: JavaSparkContext,
generator: RandomDataGenerator[Double],
numRows: Long,
numCols: Int,
numPartitions: Int,
seed: Long): JavaRDD[Vector] = {
randomVectorRDD(jsc.sc, generator, numRows, numCols, numPartitions, seed).toJavaRDD()
}
/** ::
* `RandomRDDs.randomJavaVectorRDD` with the default seed.
*/
@Since("1.6.0")
def randomJavaVectorRDD(
jsc: JavaSparkContext,
generator: RandomDataGenerator[Double],
numRows: Long,
numCols: Int,
numPartitions: Int): JavaRDD[Vector] = {
randomVectorRDD(jsc.sc, generator, numRows, numCols, numPartitions).toJavaRDD()
}
/**
* `RandomRDDs.randomJavaVectorRDD` with the default number of partitions and the default seed.
*/
@Since("1.6.0")
def randomJavaVectorRDD(
jsc: JavaSparkContext,
generator: RandomDataGenerator[Double],
numRows: Long,
numCols: Int): JavaRDD[Vector] = {
randomVectorRDD(jsc.sc, generator, numRows, numCols).toJavaRDD()
}
/**
* Returns `numPartitions` if it is positive, or `sc.defaultParallelism` otherwise.
*/
private def numPartitionsOrDefault(sc: SparkContext, numPartitions: Int): Int = {
if (numPartitions > 0) numPartitions else sc.defaultMinPartitions
}
}