Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/15800#discussion_r87528327
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/MinHash.scala ---
@@ -76,7 +72,19 @@ class MinHashModel private[ml] (
@Since("2.1.0")
override protected[ml] def hashDistance(x: Vector, y: Vector): Double = {
// Since it's generated by hashing, it will be a pair of dense vectors.
- x.toDense.values.zip(y.toDense.values).map(pair => math.abs(pair._1 -
pair._2)).min
+ if (x.toDense.values.zip(y.toDense.values).exists(pair => pair._1 ==
pair._2)) {
--- End diff --
I think I do more agree on the comment from @jkbradley at
https://github.com/apache/spark/pull/15800#issuecomment-259298082, if I
understand correctly some terms here.
Is the indicator meaning a matching hashing value between two vectors from
one hashing function, i.e., h_i?
If this understanding is correct, I think averaging indicators should be
the right way to compute MinHash's hash distance.
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