Hi All,
I found that LogisticRegressionWithLBFGS interface is not consistent
with LogisticRegressionWithSGD in master and 1.1 release.
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala#L199
In the above code snippet,
I also found
https://github.com/apache/spark/commit/8f6e2e9df41e7de22b1d1cbd524e20881f861dd0
had resolve this issue but it seems that right code snippet not occurs in
master or 1.1 release.
2014-09-13 17:12 GMT+08:00 Yanbo Liang yanboha...@gmail.com:
Hi All,
I found that
Hi Yanbo,
We made the change here
https://github.com/apache/spark/commit/5d25c0b74f6397d78164b96afb8b8cbb1b15cfbd
Those apis to set the parameters are very difficult to maintain, so we
decide not to provide them. In next release, Spark 1.2, we will have a
better api design for parameter setting.
Hi All:
We know some memory of spark are used for computing (e.g.,
spark.shuffle.memoryFraction) and some are used for caching RDD for future
use (e.g., spark.storage.memoryFraction).
Is there any existing workload which can utilize both of them during the
running left cycle? I want to do some
