Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15621#discussion_r85137296
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala ---
@@ -137,61 +133,68 @@ private[ml] class WeightedLeastSquares(
}
}
- // scale aBar to standardized space in-place
- val aBarValues = aBar.values
- var j = 0
- while (j < numFeatures) {
- if (aStd(j) == 0.0) {
- aBarValues(j) = 0.0
- } else {
- aBarValues(j) /= aStd(j)
- }
- j += 1
- }
+ val bBar = summary.bBar / bStd
+ val bbBar = summary.bbBar / (bStd * bStd)
- // scale abBar to standardized space in-place
- val abBarValues = abBar.values
- val aStdValues = aStd.values
- j = 0
- while (j < numFeatures) {
- if (aStdValues(j) == 0.0) {
- abBarValues(j) = 0.0
- } else {
- abBarValues(j) /= (aStdValues(j) * bStd)
+ val aStd = summary.aStd
+ val aBar = {
+ val _aBar = summary.aBar
+ var i = 0
+ // scale aBar to standardized space in-place
+ while (i < numFeatures) {
+ if (aStd(i) == 0.0) {
+ _aBar.values(i) = 0.0
--- End diff --
You're probably right. The only thing it might have a real impact is
`aaBar`, but since we use while loops for speed, it seems better to also use
this optimization as well.
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