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https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15233304#comment-15233304
]
ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user danielblazevski commented on the pull request:
https://github.com/apache/flink/pull/1220#issuecomment-207685012
@hsaputra I added apache/flink as upstream, namely:
`git remote add upstream https://github.com/apache/flink.git`
Then I ran what Chiwan above suggested, namely:
```
# 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
```
I then moved the 4 knn files originally in flink-staging/ to
flink-libraries/ and pushed again.
The unfortunate thing now is that when I run `mvn clean package
-DskipTests` I get errors (I can show you if you'd like....but I assume the
Travic CI build won't go through and the error will pop up there too). Did I
do something wrong? The good news is that I made a copy of the directory that
I was working in since I've had rebasing problems before, so I can always try
to go back to that and do a force push.
I wonder since I'm only adding new files whether it's even easier to just
clone `apache/master`, run `mvn clean package -DskipTests` put the new files in
there and submit a new PR?
> 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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