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https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14948385#comment-14948385
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ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user tillrohrmann commented on the pull request:
https://github.com/apache/flink/pull/1220#issuecomment-146474169
A more scalaesque solution for the `partitionBox` method without using vars
and for loops would be
```
def partitionBox(center: Vector, width: Vector): Seq[Vector] = {
def partitionHelper(box: Seq[Vector], dim: Int): Seq[Vector] = {
if (dim >= width.size) {
box
} else {
val newBox = box.flatMap {
vector =>
val (up, down) = (vector.copy, vector)
up.update(dim, up(dim) - width(dim) / 4)
down.update(dim, down(dim) + width(dim) / 4)
Seq(up, down)
}
partitionHelper(newBox, dim + 1)
}
}
partitionHelper(Seq(center), 0)
}
```
> 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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