Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15018#discussion_r94640811
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
---
@@ -328,74 +336,80 @@ 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
+ // Keeps track of the start and end indices of the blocks. if [i, j]
is a valid block from
+ // input(i) to input(j) (inclusive), then blockBounds(i) = j and
blockBounds(j) = i
+ val blockBounds = Array.range(0, input.length) // Initially, each data
point is its own block
- var i = start
- while (i <= end) {
- input(i) = (weightedSum / weight, input(i)._2, input(i)._3)
- i = i + 1
- }
+ // 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 { case (y, _, weight)
=>
+ require(weight != 0.0)
--- End diff --
Are negative weights OK? (you don't need a type on `weight`, but hardly
matters)
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
