Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-67275869
The optimizer parameter doesn't have to be a string. It could be an
optimizer type as in GeneralizedLinearAlgorithm. The optimizer type can store
whatever parameters it needs. This is a more modular setup than separate
train() methods or passing strings; it won't require new code in ANN if a new
optimizer is added.
If you want to rule out certain optimizers, then doing a match-case on the
optimizer type would be reasonable (in a setOptimizer() method in ANN).
How does that sound?
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