[
https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15819810#comment-15819810
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ASF GitHub Bot commented on MADLIB-927:
---------------------------------------
Github user orhankislal commented on the issue:
https://github.com/apache/incubator-madlib/pull/81
Not sure how to tackle. It is interesting that you don't get any actual
errors but a simple confirmation. It seems them makefile (generated by cmake)
doesn't even try to build anything. Could you paste the results of `du -h
doc/`? Maybe the folder sizes will point us to somewhere. For reference, here
is my output (taken right after the cmake)
```
du -h doc/
8.0K doc//bin/CMakeFiles
28K doc//bin
16K doc//CMakeFiles/devdoc.dir
16K doc//CMakeFiles/doc.dir
36K doc//CMakeFiles/doxysql.dir
16K doc//CMakeFiles/update_mathjax.dir
92K doc//CMakeFiles
16K doc//design/CMakeFiles/auxclean.dir
16K doc//design/CMakeFiles/design.dir
20K doc//design/CMakeFiles/design_auxclean.dir
36K doc//design/CMakeFiles/design_dvi.dir
16K doc//design/CMakeFiles/design_html.dir
36K doc//design/CMakeFiles/design_pdf.dir
36K doc//design/CMakeFiles/design_ps.dir
16K doc//design/CMakeFiles/design_safepdf.dir
16K doc//design/CMakeFiles/dvi.dir
16K doc//design/CMakeFiles/html.dir
16K doc//design/CMakeFiles/pdf.dir
16K doc//design/CMakeFiles/ps.dir
16K doc//design/CMakeFiles/safepdf.dir
280K doc//design/CMakeFiles
0B doc//design/figures
0B doc//design/modules
0B doc//design/other-chapters
300K doc//design
8.0K doc//etc/CMakeFiles
144K doc//etc
4.0K doc//imgs
596K doc/
```
> 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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