Github user bgreeven commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-67422794
@jkbradley @Lewuathe
Indeed I have been thinking about such interface as well. I quite like it,
but...:
GradientDescent is private in MLLIB, so you can't create a GradientDescent
object from the application:
```
class GradientDescent private[mllib] (private var gradient: Gradient,
private var updater: Updater)
extends Optimizer with Logging
```
That can be easily resolved by removing `private[mllib]` though.
What worries me more, is that both of the GradientDescent and LBFGS classes
require the gradient and updater as parameters. However, we define a customised
gradient and updater for ANN, which may be best to keep private for the ANN.
What do you think?
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