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https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15832161#comment-15832161
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ASF GitHub Bot commented on MADLIB-927:
---------------------------------------
Github user njayaram2 commented on the issue:
https://github.com/apache/incubator-madlib/pull/81
Hi Auon,
If you plan to make more changes and update your PR, I suggest you update
your master and rebase it on your knn branch. Assuming "master" refers to
apache/incubator-madlib in your setup, do the following AFTER you make
changes to the knn feature branch and committing them, but BEFORE pushing
it:
Assuming you are in features/knn branch, and have committed your changes.
- git checkout master
- git pull --ff-only
- git checkout features/knn
- git rebase master
Now you can push the code
(A useful git command to visualize where your HEAD and various branches
are, is: git log --graph --decorate --oneline --all)
- git push <your-repo-name>
I think your local master is a few commits behind the
apache/incubator-madlib master. It is good to do the above before you push
any code to your feature branch.
NJ
On Fri, Jan 20, 2017 at 9:28 AM, Orhan Kislal <[email protected]>
wrote:
> Hi Auon,
> My suggestion is to give them a try and if you agree with the content,
> merge them.
> Here is a small list of validations (I know you covered some of them in
> the code):
>
> - Every input should be checked for null
> - Every string should be checked for empty string ''
> - Columns should exist in their respective tables
> - Input Tables should not be empty
> - Output tables should not exist
> Thanks
> Orhan
>
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> Initial implementation of k-NN
> ------------------------------
>
> Key: MADLIB-927
> URL: https://issues.apache.org/jira/browse/MADLIB-927
> Project: Apache MADlib
> Issue Type: New Feature
> Reporter: Rahul Iyer
> Labels: gsoc2016, starter
>
> k-Nearest Neighbors is a simple algorithm based on finding nearest neighbors
> of data points in a metric feature space according to a specified distance
> function. It is considered one of the canonical algorithms of data science.
> It is a nonparametric method, which makes it applicable to a lot of
> real-world problems where the data doesn’t satisfy particular distribution
> assumptions. It can also be implemented as a lazy algorithm, which means
> there is no training phase where information in the data is condensed into
> coefficients, but there is a costly testing phase where all data (or some
> subset) is used to make predictions.
> This JIRA involves implementing the naïve approach - i.e. compute the k
> nearest neighbors by going through all points.
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