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https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14946736#comment-14946736
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ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
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.
> 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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