Github user alanctgardner commented on a diff in the pull request: https://github.com/apache/spark/pull/3626#discussion_r21402907 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala --- @@ -65,6 +65,24 @@ class NaiveBayesModel private[mllib] ( override def predict(testData: Vector): Double = { labels(brzArgmax(brzPi + brzTheta * testData.toBreeze)) } + + def classProbabilities(testData: RDD[Vector]): + RDD[scala.collection.mutable.Map[Double, Double]] = { + val bcModel = testData.context.broadcast(this) + testData.mapPartitions { iter => + val model = bcModel.value + iter.map(model.classProbabilities) + } + } + + def classProbabilities(testData: Vector): scala.collection.mutable.Map[Double, Double] = { --- End diff -- Scala newbie. I couldn't find a better pattern to build the map than mutating it in the foreach. Should I just build a map then make it immutable for returning?
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