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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1745) Add exact k-nearest-neighbours algorithm to machine learning library
Date Wed, 14 Oct 2015 17:28:05 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14957329#comment-14957329
] 

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

Github user danielblazevski commented on the pull request:

    https://github.com/apache/flink/pull/1220#issuecomment-148125833
  
    @tillrohrmann I fixed a couple of typos and verified that center/width of the root node
are what they should be based on the input of MinVec and MaxVec.  `partitionBox` is not running
in an infinite loop, but insertRecur is, which seems due to the output of `partitionBox`
    
    ```
    center = DenseVector(1.0, -0.5)
    width = DenseVector(0.0, 0.0)
    box    List(DenseVector(1.0, -0.5), DenseVector(1.0, -0.5), DenseVector(1.0, -0.5), DenseVector(1.0,
-0.5))
    ```
    The output is `box` printed out on the base-case of the recursion, namely:
    ```scala
           if (dim >= width.size) {
              println("box    " + box)
              box
    ```
    The centers are not computed correctly.


> 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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