[
https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14946878#comment-14946878
]
ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user chiwanpark commented on the pull request:
https://github.com/apache/flink/pull/1220#issuecomment-146202529
It sounds weird for me. If the user sets `useQuadTree` to false, the
algorithm should not use quadtree. Otherwise if the user sets `useQuadTree` to
true, the algorithm should check whether quadtree can be used or not.
I don't think that `ListBuffer` is better than `Vector`. For example, we
can implement `partitionBox` like following:
```scala
def partitionBox(cPart: Seq[Vector], L: Vector): Seq[Vector] = {
var next = cPart
(0 until L.size).foreach { i =>
next = next.flatMap { v =>
val (up, down) = (v.copy, v)
up.update(i, up(i) - L(i) / 4)
down.update(i, down(i) + L(i) / 4)
Seq(up, down)
}
}
next
}
```
There are still some style issues in this PR. I recommend reformatting all
codes in this PR using IDE such as IntelliJ IDEA.
> Add exact k-nearest-neighbours algorithm to machine learning library
> --------------------------------------------------------------------
>
> Key: FLINK-1745
> URL: https://issues.apache.org/jira/browse/FLINK-1745
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Daniel Blazevski
> Labels: ML, Starter
>
> Even though the k-nearest-neighbours (kNN) [1,2] algorithm is quite trivial
> it is still used as a mean to classify data and to do regression. This issue
> focuses on the implementation of an exact kNN (H-BNLJ, H-BRJ) algorithm as
> proposed in [2].
> Could be a starter task.
> Resources:
> [1] [http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]
> [2] [https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf]
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