Github user feynmanliang commented on the pull request:
https://github.com/apache/spark/pull/7307#issuecomment-120212795
Actually, I don't think that generalizing eta to asymmetric priors is a
good idea.
Eta is a `num_topics` by `num_words` matrix where each row's entries are
Dirichlet parameters for a topic's distribution over word distributions, so
prespecifying eta would requiring knowing the num_words in the vocabulary at
the time of model initialization.
Alpha was okay because it is `k`-dimensional and the user specifies `k`
during model initialization.
The other option would be for the user to specify the vocabulary size prior
to runtime during LDA model initialization.
@jkbradley thoughts?
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