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From danielblazevski <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-1745] Add exact k-nearest-neighbours al...
Date Wed, 07 Oct 2015 12:01:46 GMT
Github user danielblazevski commented on the pull request:

    @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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