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https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14942703#comment-14942703
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ASF GitHub Bot commented on FLINK-1745:
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Github user danielblazevski commented on the pull request:
https://github.com/apache/flink/pull/1220#issuecomment-145364401
Thanks @chiwanpark for the very useful comments. I have made changes to
the comments, which can be found here:
https://github.com/danielblazevski/flink/tree/FLINK-1745/flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/nn
I also changed the testing of KNN + QuadTree, which can be found here:
https://github.com/danielblazevski/flink/tree/FLINK-1745/flink-staging/flink-ml/src/test/scala/org/apache/flink/ml/nn
Since useQuadTree is now a parameter, I did not need KNNQuadTreeSuite
anymore and I removed it.
I did not address comment 6 yet. I need to have the training set before I
can define a non-user specified useQuadTree, so any main if(useQuadTree) should
come within ` val crossed = trainingSet.cross(inputSplit).mapPartition {`
About your last "P.S" comment, Creating the quadtree after the cross
operation is likely more efficient -- each CPU/Node will form their own
quadtree, which is what is suggested for the R-tree here:
https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
This will result less communication overhead than creating a more global
quadtree, if that is what you were referring to.
> 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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