Github user danielblazevski commented on the pull request:
https://github.com/apache/flink/pull/1220#issuecomment-146175315
@chiwanpark, in lines 203-207
+ val useQuadTree =
resultParameters.get(useQuadTreeParam).getOrElse(
+ training.values.head.size +
math.log(math.log(training.values.length)/
+ math.log(4.0)) <
math.log(training.values.length)/math.log(4.0) &&
+ (metric.isInstanceOf[EuclideanDistanceMetric] ||
+ metric.isInstanceOf[SquaredEuclideanDistanceMetric]))
the code decides whether to use quadtree or not if no value is specified.
This codes decides based on the number of training + test points + dimension,
and is a conservative estimate so that when it uses the quadtree, the quadtree
will improve performance compared to the brute-force method -- basically the
quadtree scales poorly with dimension, but really well with the number of
points.
As for using a `Vector` for `minVec` and `maxVec`, I plug in `minVec` and
`maxVec` to construct the root Node, and I found it best to use a ListBuffer in
the constructor for the Node class when partitioning the boxes into sub-boxes.
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