Merge pull request #722 from JoshRosen/spark-825

Fix bug: DoubleRDDFunctions.sampleStdev() computed non-sample stdev()

core/src/main/scala/spark/DoubleRDDFunctions.scala

@@ -54,7 +54,13 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * Compute the sample standard deviation of this RDD's elements (which corrects for bias in
    * estimating the standard deviation by dividing by N-1 instead of N).
    */
-  def sampleStdev(): Double = stats().stdev
+  def sampleStdev(): Double = stats().sampleStdev
+
+  /**
+   * Compute the sample variance of this RDD's elements (which corrects for bias in
+   * estimating the variance by dividing by N-1 instead of N).
+   */
+  def sampleVariance(): Double = stats().sampleVariance
 
   /** (Experimental) Approximate operation to return the mean within a timeout. */
   def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {

core/src/main/scala/spark/api/java/JavaDoubleRDD.scala

@@ -115,33 +115,48 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav
 
   // Double RDD functions
 
-  /** Return the sum of the elements in this RDD. */
+  /** Add up the elements in this RDD. */
   def sum(): Double = srdd.sum()
 
-  /** Return a [[spark.StatCounter]] describing the elements in this RDD. */
+  /**
+   * Return a [[spark.util.StatCounter]] object that captures the mean, variance and count
+   * of the RDD's elements in one operation.
+   */
   def stats(): StatCounter = srdd.stats()
 
-  /** Return the mean of the elements in this RDD. */
+  /** Compute the mean of this RDD's elements. */
   def mean(): Double = srdd.mean()
 
-  /** Return the variance of the elements in this RDD. */
+  /** Compute the variance of this RDD's elements. */
   def variance(): Double = srdd.variance()
 
-  /** Return the standard deviation of the elements in this RDD. */
+  /** Compute the standard deviation of this RDD's elements. */
   def stdev(): Double = srdd.stdev()
 
+  /**
+   * Compute the sample standard deviation of this RDD's elements (which corrects for bias in
+   * estimating the standard deviation by dividing by N-1 instead of N).
+   */
+  def sampleStdev(): Double = srdd.sampleStdev()
+
+  /**
+   * Compute the sample variance of this RDD's elements (which corrects for bias in
+   * estimating the standard variance by dividing by N-1 instead of N).
+   */
+  def sampleVariance(): Double = srdd.sampleVariance()
+
   /** Return the approximate mean of the elements in this RDD. */
   def meanApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble] =
     srdd.meanApprox(timeout, confidence)
 
-  /** Return the approximate mean of the elements in this RDD. */
+  /** (Experimental) Approximate operation to return the mean within a timeout. */
   def meanApprox(timeout: Long): PartialResult[BoundedDouble] = srdd.meanApprox(timeout)
 
-  /** Return the approximate sum of the elements in this RDD. */
+  /** (Experimental) Approximate operation to return the sum within a timeout. */
   def sumApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble] =
     srdd.sumApprox(timeout, confidence)
- 
-  /** Return the approximate sum of the elements in this RDD. */
+
+  /** (Experimental) Approximate operation to return the sum within a timeout. */
   def sumApprox(timeout: Long): PartialResult[BoundedDouble] = srdd.sumApprox(timeout)
 }
 

core/src/test/scala/spark/JavaAPISuite.java

@@ -326,7 +326,9 @@ public class JavaAPISuite implements Serializable {
     Assert.assertEquals(20/6.0, rdd.mean(), 0.01);
     Assert.assertEquals(20/6.0, rdd.mean(), 0.01);
     Assert.assertEquals(6.22222, rdd.variance(), 0.01);
+    Assert.assertEquals(7.46667, rdd.sampleVariance(), 0.01);
     Assert.assertEquals(2.49444, rdd.stdev(), 0.01);
+    Assert.assertEquals(2.73252, rdd.sampleStdev(), 0.01);
 
     Double first = rdd.first();
     List<Double> take = rdd.take(5);