Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/12016#discussion_r57723732
--- Diff: core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala
---
@@ -40,30 +41,39 @@ private[spark] class SumEvaluator(totalOutputs: Int,
confidence: Double)
override def currentResult(): BoundedDouble = {
if (outputsMerged == totalOutputs) {
new BoundedDouble(counter.sum, 1.0, counter.sum, counter.sum)
- } else if (outputsMerged == 0) {
+ } else if (outputsMerged == 0 || counter.count == 0) {
new BoundedDouble(0, 0.0, Double.NegativeInfinity,
Double.PositiveInfinity)
} else {
val p = outputsMerged.toDouble / totalOutputs
val meanEstimate = counter.mean
- val meanVar = counter.sampleVariance / counter.count
val countEstimate = (counter.count + 1 - p) / p
- val countVar = (counter.count + 1) * (1 - p) / (p * p)
val sumEstimate = meanEstimate * countEstimate
- val sumVar = (meanEstimate * meanEstimate * countVar) +
- (countEstimate * countEstimate * meanVar) +
- (meanVar * countVar)
- val sumStdev = math.sqrt(sumVar)
- val confFactor = {
- if (counter.count > 100) {
+
+ val meanVar = counter.sampleVariance / counter.count
+
+ // branch at this point because counter.count == 1 implies
counter.sampleVariance == Nan
+ // and we don't want to ever return a bound of NaN
+ if (meanVar == Double.NaN || counter.count == 1) {
--- End diff --
`NaN != NaN`; you'd have to use `Double.isNaN`
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