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Github user sethah commented on the issue:
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@MLnick I updated it with your suggested wording for the comments.
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Github user MLnick commented on the issue:
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@dbtsai I understand wanting to make things clear & easy to understand for
current and future developers. Of course I would like the same.
However, it's currently not clear to me how (or if)
Github user dbtsai commented on the issue:
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@MLnick I'm hoping that we could abstract out the the implementation of
using column major format as much as possible; as a result, in the future, new
developers can understand the code without think
Github user sethah commented on the issue:
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Thanks for the detailed explanation @dbtsai. +1 for doing this in a
separate PR, since I'd imagine we want to run all the performance tests again
as well.
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Github user dbtsai commented on the issue:
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Thanks all for working on this PR. I merged this into master, and I'll
create a followup task and PR to handle the abstraction together with handling
the smoothing in the initialization of coefficien
Github user sethah commented on the issue:
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Thanks @dbtsai!
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Github user MLnick commented on the issue:
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Thanks @dbtsai for your reply - yes that makes things clear. I didn't
realise we can use a Breeze matrix in the optimizers. That will definitely
help. I agree we should try to do that and simplify &
Github user sethah commented on the issue:
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cc @dbtsai
This may improve the problems you mentioned you were having on
[SPARK-17134](https://issues.apache.org/jira/browse/SPARK-17134) :)
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Github user sethah commented on the issue:
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ping @MLnick @jkbradley This should be a nice performance boost for MLOR in
ML, hopefully we can get it in for 2.1. If you get some time to review or run
tests I'd really appreciate it.
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Github user dbtsai commented on the issue:
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@sethah I'm recently busy on company work. Will start to work on open
source code review soon this week.
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