Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12402#discussion_r60466864
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala ---
@@ -105,6 +108,15 @@ class GaussianMixtureModel private[ml] (
def gaussians: Array[MultivariateGaussian] = parentModel.gaussians
@Since("2.0.0")
--- End diff --
I agree it simplifies things when the return result is either a basic type
or a DataFrame. I think it makes sense for topics and synonyms, for which
there is no "natural" representation. But for distributions, there is a
natural class to provide (the MultivariateGaussian class, with its associated
methods like pdf). I'd also be OK with providing only a dataframe for now in
Python, perhaps using a method called ```gaussiansDF```.
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