fix minor error

Author: yinxusen

mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDFunctions.scala

@@ -27,11 +27,35 @@ import org.apache.spark.rdd.RDD
  * count.
  */
 trait VectorRDDStatisticalSummary {
+
+  /**
+   * Computes the mean of columns in RDD[Vector].
+   */
   def mean: Vector
+
+  /**
+   * Computes the sample variance of columns in RDD[Vector].
+   */
   def variance: Vector
+
+  /**
+   * Computes number of vectors in RDD[Vector].
+   */
   def count: Long
+
+  /**
+   * Computes the number of non-zero elements in each column of RDD[Vector].
+   */
   def numNonZeros: Vector
+
+  /**
+   * Computes the maximum of each column in RDD[Vector].
+   */
   def max: Vector
+
+  /**
+   * Computes the minimum of each column in RDD[Vector].
+   */
   def min: Vector
 }
 
@@ -53,7 +77,6 @@ private class VectorRDDStatisticsAggregator(
     val currMin: BDV[Double])
   extends VectorRDDStatisticalSummary with Serializable {
 
-  // lazy val is used for computing only once time. Same below.
   override def mean = {
     val realMean = BDV.zeros[Double](currMean.length)
     var i = 0
@@ -71,7 +94,7 @@ private class VectorRDDStatisticsAggregator(
     while (i < currM2n.size) {
       realVariance(i) =
         currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt
-      realVariance(i) /= totalCnt
+      realVariance(i) /= (totalCnt - 1.0)
       i += 1
     }
     Vectors.fromBreeze(realVariance)

mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDFunctionsSuite.scala

@@ -34,8 +34,8 @@ class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
 
   val localData = Array(
     Vectors.dense(1.0, 2.0, 3.0),
-    Vectors.dense(4.0, 5.0, 6.0),
-    Vectors.dense(7.0, 8.0, 9.0)
+    Vectors.dense(4.0, 0.0, 6.0),
+    Vectors.dense(0.0, 8.0, 9.0)
   )
 
   val sparseData = ArrayBuffer(Vectors.sparse(3, Seq((0, 1.0))))
@@ -47,21 +47,21 @@ class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
     val data = sc.parallelize(localData, 2)
     val summary = data.computeSummaryStatistics()
 
-    assert(equivVector(summary.mean, Vectors.dense(4.0, 5.0, 6.0)),
+    assert(equivVector(summary.mean, Vectors.dense(5.0 / 3.0, 10.0 / 3.0, 6.0)),
       "Dense column mean do not match.")
 
-    assert(equivVector(summary.variance, Vectors.dense(6.0, 6.0, 6.0)),
+    assert(equivVector(summary.variance, Vectors.dense(4.333333333333334, 17.333333333333336, 9.0)),
       "Dense column variance do not match.")
 
     assert(summary.count === 3, "Dense column cnt do not match.")
 
-    assert(equivVector(summary.numNonZeros, Vectors.dense(3.0, 3.0, 3.0)),
+    assert(equivVector(summary.numNonZeros, Vectors.dense(2.0, 2.0, 3.0)),
       "Dense column nnz do not match.")
 
-    assert(equivVector(summary.max, Vectors.dense(7.0, 8.0, 9.0)),
+    assert(equivVector(summary.max, Vectors.dense(4.0, 8.0, 9.0)),
       "Dense column max do not match.")
 
-    assert(equivVector(summary.min, Vectors.dense(1.0, 2.0, 3.0)),
+    assert(equivVector(summary.min, Vectors.dense(0.0, 0.0, 3.0)),
       "Dense column min do not match.")
   }
 
@@ -72,7 +72,7 @@ class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
     assert(equivVector(summary.mean, Vectors.dense(0.06, 0.05, 0.0)),
       "Sparse column mean do not match.")
 
-    assert(equivVector(summary.variance, Vectors.dense(0.2564, 0.2475, 0.0)),
+    assert(equivVector(summary.variance, Vectors.dense(0.258989898989899, 0.25, 0.0)),
       "Sparse column variance do not match.")
 
     assert(summary.count === 100, "Sparse column cnt do not match.")
@@ -90,6 +90,6 @@ class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
 
 object VectorRDDFunctionsSuite {
   def equivVector(lhs: Vector, rhs: Vector): Boolean = {
-    (lhs.toBreeze - rhs.toBreeze).norm(2) < 1e-9
+    (lhs.toBreeze - rhs.toBreeze).norm(2) < 1e-5
   }
 }