[SPARK-4891][PySpark][MLlib] Add gamma/log normal/exp dist sampling to P...

...ySpark MLlib

This is a follow up to PR3680 https://github.com/apache/spark/pull/3680 .

Author: RJ Nowling <rnowling@gmail.com>

Closes #3955 from rnowling/spark4891 and squashes the following commits:

1236a01 [RJ Nowling] Fix Python style issues
7a01a78 [RJ Nowling] Fix Python style issues
174beab [RJ Nowling] [SPARK-4891][PySpark][MLlib] Add gamma/log normal/exp dist sampling to PySpark MLlib

1 files changed, 88 insertions, 0 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index c4e5fd8e46..555da8c7e7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -625,6 +625,21 @@ class PythonMLLibAPI extends Serializable {
   }
 
   /**
+   * Java stub for Python mllib RandomRDDGenerators.logNormalRDD()
+   */
+  def logNormalRDD(jsc: JavaSparkContext,
+      mean: Double,
+      std: Double,
+      size: Long,
+      numPartitions: java.lang.Integer,
+      seed: java.lang.Long): JavaRDD[Double] = {
+    val parts = getNumPartitionsOrDefault(numPartitions, jsc)
+    val s = getSeedOrDefault(seed)
+    RG.logNormalRDD(jsc.sc, mean, std, size, parts, s)
+  }
+
+
+  /**
    * Java stub for Python mllib RandomRDDGenerators.poissonRDD()
    */
   def poissonRDD(jsc: JavaSparkContext,
@@ -638,6 +653,33 @@ class PythonMLLibAPI extends Serializable {
   }
 
   /**
+   * Java stub for Python mllib RandomRDDGenerators.exponentialRDD()
+   */
+  def exponentialRDD(jsc: JavaSparkContext,
+      mean: Double,
+      size: Long,
+      numPartitions: java.lang.Integer,
+      seed: java.lang.Long): JavaRDD[Double] = {
+    val parts = getNumPartitionsOrDefault(numPartitions, jsc)
+    val s = getSeedOrDefault(seed)
+    RG.exponentialRDD(jsc.sc, mean, size, parts, s)
+  }
+
+  /**
+   * Java stub for Python mllib RandomRDDGenerators.gammaRDD()
+   */
+  def gammaRDD(jsc: JavaSparkContext,
+      shape: Double,
+      scale: Double,
+      size: Long,
+      numPartitions: java.lang.Integer,
+      seed: java.lang.Long): JavaRDD[Double] = {
+    val parts = getNumPartitionsOrDefault(numPartitions, jsc)
+    val s = getSeedOrDefault(seed)
+    RG.gammaRDD(jsc.sc, shape, scale, size, parts, s)
+  }
+
+  /**
    * Java stub for Python mllib RandomRDDGenerators.uniformVectorRDD()
    */
   def uniformVectorRDD(jsc: JavaSparkContext,
@@ -664,6 +706,22 @@ class PythonMLLibAPI extends Serializable {
   }
 
   /**
+   * Java stub for Python mllib RandomRDDGenerators.logNormalVectorRDD()
+   */
+  def logNormalVectorRDD(jsc: JavaSparkContext,
+      mean: Double,
+      std: Double,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: java.lang.Integer,
+      seed: java.lang.Long): JavaRDD[Vector] = {
+    val parts = getNumPartitionsOrDefault(numPartitions, jsc)
+    val s = getSeedOrDefault(seed)
+    RG.logNormalVectorRDD(jsc.sc, mean, std, numRows, numCols, parts, s)
+  }
+
+
+  /**
    * Java stub for Python mllib RandomRDDGenerators.poissonVectorRDD()
    */
   def poissonVectorRDD(jsc: JavaSparkContext,
@@ -677,6 +735,36 @@ class PythonMLLibAPI extends Serializable {
     RG.poissonVectorRDD(jsc.sc, mean, numRows, numCols, parts, s)
   }
 
+  /**
+   * Java stub for Python mllib RandomRDDGenerators.exponentialVectorRDD()
+   */
+  def exponentialVectorRDD(jsc: JavaSparkContext,
+      mean: Double,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: java.lang.Integer,
+      seed: java.lang.Long): JavaRDD[Vector] = {
+    val parts = getNumPartitionsOrDefault(numPartitions, jsc)
+    val s = getSeedOrDefault(seed)
+    RG.exponentialVectorRDD(jsc.sc, mean, numRows, numCols, parts, s)
+  }
+
+  /**
+   * Java stub for Python mllib RandomRDDGenerators.gammaVectorRDD()
+   */
+  def gammaVectorRDD(jsc: JavaSparkContext,
+      shape: Double,
+      scale: Double,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: java.lang.Integer,
+      seed: java.lang.Long): JavaRDD[Vector] = {
+    val parts = getNumPartitionsOrDefault(numPartitions, jsc)
+    val s = getSeedOrDefault(seed)
+    RG.gammaVectorRDD(jsc.sc, shape, scale, numRows, numCols, parts, s)
+  }
+
+
 }
 
 /**