[GitHub] [spark] zhengruifeng commented on a change in pull request #24963: [SPARK-28159][ML] Make the transform natively in ml framework to avoid extra conversion

[GitBox <gi...@apache.org>]

zhengruifeng commented on a change in pull request #24963: [SPARK-28159][ML] Make the transform natively in ml framework to avoid extra conversion
URL: https://github.com/apache/spark/pull/24963#discussion_r299777200
 
 

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 File path: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
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 @@ -642,6 +639,20 @@ private[clustering] object OnlineLDAOptimizer {
     }
 
     val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector /:/ phiNorm).asDenseMatrix
-    (gammad, sstatsd, ids)
+    (gammad, sstatsd, indices)
+  }
+
+  private[clustering] def variationalTopicInference(
+      termCounts: Vector,
+      expElogbeta: BDM[Double],
+      alpha: breeze.linalg.Vector[Double],
+      gammaShape: Double,
+      k: Int,
+      seed: Long): (BDV[Double], BDM[Double], List[Int]) = {
+    val (ids: List[Int], cts: Array[Double]) = termCounts match {
+      case v: DenseVector => ((0 until v.size).toList, v.values)
 
 Review comment:
   I am afraid that an empty list may not help to simplify the impl.
   since in place like `private[clustering] def submitMiniBatch(batch: RDD[(Long, Vector)]): OnlineLDAOptimizer`,  we still have to create a `List` for slicing.

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