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https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15114638#comment-15114638
]
ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user chiwanpark commented on the pull request:
https://github.com/apache/flink/pull/1220#issuecomment-174369547
Hi @danielblazevski, you don't need to open a new PR and merge master
branch. Instead, you update `master` branch and rebase your local `FLINK-1745`
branch on `master` branch. After doing rebase, you have to force push on your
github `FLINK-1745` branch.
```bash
# fetch updated master branch
git fetch upstream master
# checkout local master branch
git checkout master
# merge local master branch and upstream master branch (this should be
fast-forward merge.)
git merge upstream/master
# checkout local FLINK-1745 branch
git checkout FLINK-1745
# rebase FLINK-1745 on local master branch
git rebase master
# force push local FLINK-1745 branch to github's FLINK-1745 branch
git push origin +FLINK-1745
```
Note that there is `+` before `FLINK-1745` to force push.
About raising error, I think the user specifies all parameters before
calling `fit` method in typical case. Currently, the error will raise doing
cross operation because checking metric is in `minDist` method of `QuadTree`
class. I would like to check this metric conflict before doing operation. It is
best to add a method like `checkQuadTreeConflict` in `KNN` class and call it in
`setUseQuadTree` and `setDistanceMetric` method or call it in anyway before
doing 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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