Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15018#discussion_r92920572
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
---
@@ -328,74 +336,68 @@ class IsotonicRegression private (private var
isotonic: Boolean) extends Seriali
return Array.empty
}
- // Pools sub array within given bounds assigning weighted average
value to all elements.
- def pool(input: Array[(Double, Double, Double)], start: Int, end:
Int): Unit = {
- val poolSubArray = input.slice(start, end + 1)
-
- val weightedSum = poolSubArray.map(lp => lp._1 * lp._3).sum
- val weight = poolSubArray.map(_._3).sum
-
- var i = start
- while (i <= end) {
- input(i) = (weightedSum / weight, input(i)._2, input(i)._3)
- i = i + 1
- }
+ /*
+ Keeps track of the start and end indices of the blocks.
blockBounds(start) gives the
+ index of the end of the block and blockBounds(end) gives the index of
the start of the
+ block. Entries that are not the start or end of the block are
meaningless.
+ */
+ val blockBounds = Array.range(0, input.length) // Initially, each data
point is its own block
+
+ /*
+ Keep track of the sum of weights and sum of weight * y for each block.
weights(start)
+ gives the values for the block. Entries that are not at the start of a
block
+ are meaningless.
+ */
+ val weights: Array[(Double, Double)] = input.map(x => (x._3, x._3 *
x._1)) // (weight, weight * y)
--- End diff --
Up to you; might be clearer as `input.map { case (y, _, weight) => (weight,
weight * y) }`
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