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https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15819191#comment-15819191
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ASF GitHub Bot commented on MADLIB-927:
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Github user orhankislal commented on the issue:
https://github.com/apache/incubator-madlib/pull/81
Yes, the section that starts with `@addtogroup` is the documentation that
will be reflected on the website when the pr is merged in the the repo. You
will need latex installed on your machine as well as a gnu gcc (Apple's
compiler doesn't work). You can start by a copy-paste from an existing module
and replace the content as needed. The doc is compiled by `make doc` command
and the output html files will be in the build folder for inspection. If the
command doesn't work you can still submit the changes so that I can compile and
alter it if needed.
I really appreciate your contribution in this regard. I know writing the
docs is a boring job but it is very important for the usability of MADlib.
> 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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