Github user rotationsymmetry commented on the pull request:
https://github.com/apache/spark/pull/8022#issuecomment-135557506
@feynmanliang Thank you very much for your review.
I have incorporated your comments in commit a4ed2b0.
* Add ScalaDoc for public API.
* Add ScalaDoc to decribe the forgetful algorithm in
StreamingLinearAlgorithm.
* Remove F-polymorphism in StreamingDecaySetter[T].
* decayFactor and timeUnit in StreamingDecaySetter[T] are now private.
* Remove division by zero in trainOn of StreamingLinearAlgorithm; provide
comments to explains why.
* Improve testing cases of StreamingLogisticRegressionSuite to have rel
tol=0.1.
* resolve merge conflict.
As for your comment of "having getLambda instead of getDiscount in
StreamingDecay", I feel that the discount factor better conveys the
mathematical idea of the algorithm. Lambda, on the other hand, is only a
temporary value in the calculation. For example, in the [spark
doc](http://spark.apache.org/docs/latest/mllib-clustering.html#streaming-k-means),
the discount factor is employed to describe the algorithm. I have included
similar description in the ScalaDoc for `StreamingLinearAlgorithm`.
Thanks again for your review. If you have any further comments, please let
me know.
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