[jira] [Commented] (FLINK-1745) Add exact k-nearest-neighbours algorithm to machine learning library

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15093731#comment-15093731 ] 

ASF GitHub Bot commented on FLINK-1745:
---------------------------------------

Github user chiwanpark commented on the pull request:

    https://github.com/apache/flink/pull/1220#issuecomment-170875879
  
    Hi @danielblazevski, I'm sorry for late reply. If you turn off IntelliJ IDEA align option (Turn off Preferences -> Editor -> Code Style -> Scala -> Wrapping and Braces -> Method declaration parameters -> Align when multiline), you can get style that is suggested by @tillrohrmann.
    
    Could you apply this option?
    
    I think that your PR is almost ready to merge. But I have to check few problems that still exist.
    
    First, about a meaning of `UseQuadTree` parameter, you said that it means force-use quadtree. I think this would be a problem because `DistanceMeasure` parameter can be conflict with quadtree. I would like to raise an error earlier if the parameter setting has a problem. Could you add this into top of fit operation?
    
    Second, how about avoiding `cross` operation? As @tillrohrmann said, `cross` operation is a very heavy operation. Is there any nicer solution to this?
    
    Other problems such as some difference styles, unnecessary spaces can be addressed by me before merge this. :)


> 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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