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Posted to commits@spark.apache.org by me...@apache.org on 2015/09/09 05:54:04 UTC
spark git commit: [SPARK-10464] [MLLIB] Add WeibullGenerator for
RandomDataGenerator
Repository: spark
Updated Branches:
refs/heads/master 52fe32f6a -> a1573489a
[SPARK-10464] [MLLIB] Add WeibullGenerator for RandomDataGenerator
Add WeibullGenerator for RandomDataGenerator.
#8611 need use WeibullGenerator to generate random data based on Weibull distribution.
Author: Yanbo Liang <yb...@gmail.com>
Closes #8622 from yanboliang/spark-10464.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/a1573489
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/a1573489
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/a1573489
Branch: refs/heads/master
Commit: a1573489a37def97b7c26b798898ffbbdc4defa8
Parents: 52fe32f
Author: Yanbo Liang <yb...@gmail.com>
Authored: Tue Sep 8 20:54:02 2015 -0700
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Tue Sep 8 20:54:02 2015 -0700
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.../mllib/random/RandomDataGenerator.scala | 27 ++++++++++++++++++--
.../mllib/random/RandomDataGeneratorSuite.scala | 16 +++++++++++-
2 files changed, 40 insertions(+), 3 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/a1573489/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
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diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
index a2d85a6..9eab7ef 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
@@ -17,8 +17,7 @@
package org.apache.spark.mllib.random
-import org.apache.commons.math3.distribution.{ExponentialDistribution,
- GammaDistribution, LogNormalDistribution, PoissonDistribution}
+import org.apache.commons.math3.distribution._
import org.apache.spark.annotation.{Since, DeveloperApi}
import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
@@ -195,3 +194,27 @@ class LogNormalGenerator @Since("1.3.0") (
@Since("1.3.0")
override def copy(): LogNormalGenerator = new LogNormalGenerator(mean, std)
}
+
+/**
+ * :: DeveloperApi ::
+ * Generates i.i.d. samples from the Weibull distribution with the
+ * given shape and scale parameter.
+ *
+ * @param alpha shape parameter for the Weibull distribution.
+ * @param beta scale parameter for the Weibull distribution.
+ */
+@DeveloperApi
+class WeibullGenerator(
+ val alpha: Double,
+ val beta: Double) extends RandomDataGenerator[Double] {
+
+ private val rng = new WeibullDistribution(alpha, beta)
+
+ override def nextValue(): Double = rng.sample()
+
+ override def setSeed(seed: Long): Unit = {
+ rng.reseedRandomGenerator(seed)
+ }
+
+ override def copy(): WeibullGenerator = new WeibullGenerator(alpha, beta)
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/a1573489/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
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diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
index a5ca151..8416771 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
@@ -17,7 +17,7 @@
package org.apache.spark.mllib.random
-import scala.math
+import org.apache.commons.math3.special.Gamma
import org.apache.spark.SparkFunSuite
import org.apache.spark.util.StatCounter
@@ -136,4 +136,18 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
distributionChecks(gamma, expectedMean, expectedStd, 0.1)
}
}
+
+ test("WeibullGenerator") {
+ List((1.0, 2.0), (2.0, 3.0), (2.5, 3.5), (10.4, 2.222)).map {
+ case (alpha: Double, beta: Double) =>
+ val weibull = new WeibullGenerator(alpha, beta)
+ apiChecks(weibull)
+
+ val expectedMean = math.exp(Gamma.logGamma(1 + (1 / alpha))) * beta
+ val expectedVariance = math.exp(
+ Gamma.logGamma(1 + (2 / alpha))) * beta * beta - expectedMean * expectedMean
+ val expectedStd = math.sqrt(expectedVariance)
+ distributionChecks(weibull, expectedMean, expectedStd, 0.1)
+ }
+ }
}
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