Github user acidghost commented on a diff in the pull request:
https://github.com/apache/spark/pull/6761#discussion_r32807724
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -93,26 +95,60 @@ class NaiveBayesModel private[mllib] (
override def predict(testData: Vector): Double = {
modelType match {
case Multinomial =>
- val prob = thetaMatrix.multiply(testData)
- BLAS.axpy(1.0, piVector, prob)
+ val prob = multinomialCalculation(testData)
labels(prob.argmax)
case Bernoulli =>
- testData.foreachActive { (index, value) =>
- if (value != 0.0 && value != 1.0) {
- throw new SparkException(
- s"Bernoulli naive Bayes requires 0 or 1 feature values but
found $testData.")
- }
- }
- val prob = thetaMinusNegTheta.get.multiply(testData)
- BLAS.axpy(1.0, piVector, prob)
- BLAS.axpy(1.0, negThetaSum.get, prob)
+ val prob = bernoulliCalculation(testData)
labels(prob.argmax)
- case _ =>
- // This should never happen.
- throw new UnknownError(s"Invalid modelType: $modelType.")
}
}
+ def predictProbabilities(testData: RDD[Vector]): RDD[Map[Double,
Double]] = {
--- End diff --
So, in the end, should I make it return a `Vector` or a `Map`?
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