Github user akopich commented on a diff in the pull request:
https://github.com/apache/spark/pull/18924#discussion_r140180799
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala ---
@@ -503,17 +518,15 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
}
/**
- * Update alpha based on `gammat`, the inferred topic distributions for
documents in the
- * current mini-batch. Uses Newton-Rhapson method.
+ * Update alpha based on `logphat`.
--- End diff --
@WeichenXu123, you are right. So should we add `stats.count()` or should we
rather embed the counting in the aggregation phase so that we avoid the second
pass?
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