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https://issues.apache.org/jira/browse/SPARK-5400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14292176#comment-14292176
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Joseph K. Bradley commented on SPARK-5400:
------------------------------------------
I agree this could be done either way: Algorithm[Model] or Model[Algorithm].
For users, exposing the model type may be easiest; a person who is new to ML
and wants to do some clustering will know the name of a clustering model
(KMeans, GMM) but may not want to worry about picking an optimization
algorithm. So I'd vote for Model[Algorithm].
That said, internally, I agree that Algorithm[Model] would be handy for
generalizing. We could do the combination by having an internal LearningState
class:
{code}
class GaussianMixture {
def setOptimizer // once we have more than 1 optimization method
def run = {
val opt = new EM(new GMMLearningState(this))
...
}
}
private[mllib] GMMLearningState extends OurModelAbstraction {
def this(gm: GaussianMixture) = this(...)
}
class EM(model: OurModelAbstraction)
{code}
> Rename GaussianMixtureEM to GaussianMixture
> -------------------------------------------
>
> Key: SPARK-5400
> URL: https://issues.apache.org/jira/browse/SPARK-5400
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> GaussianMixtureEM is following the old naming convention of including the
> optimization algorithm name in the class title. We should probably rename it
> to GaussianMixture so that it can use other optimization algorithms in the
> future.
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