[
https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14985595#comment-14985595
]
ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user chiwanpark commented on the pull request:
https://github.com/apache/flink/pull/1220#issuecomment-153096308
I would suggest logic like following:
```scala
val useQuadTree = ~~~
if (useQuadTree) {
knnQueryWithQuadTree(training, testing, out)
} else {
knnQueryBasic(training, testing, out)
}
```
Or to reduce duplicated code in L257-L266, we can use following:
```scala
val useQuadTree = ~~~
val quadTree: Option[QuadTree] = if (useQuadTree) {
Some(buildQuadTree(training, testing))
} else {
None
}
for (a <- testing.values) {
val trainingFiltered: Seq[Vector] = quadTree match {
case Some(tree) => getSibilingsFromQuadTree(a, tree)
case None => training.values
}
for (b <- trainingFiltered) {
// (training vector, input vector, input key, distance)
queue.enqueue((b, a._2, a._1, metric.distance(b, a._2)))
if (queue.size > k) {
queue.dequeue()
}
}
for (v <- queue) {
out.collect(v)
}
}
```
In this case, we create methods about quadtree operation.
> 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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