[ 
https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15819825#comment-15819825
 ] 

ASF GitHub Bot commented on MADLIB-927:
---------------------------------------

Github user auonhaidar commented on the issue:

    https://github.com/apache/incubator-madlib/pull/81
  
    I ran this command inside build:
    $ du -h doc/
    
    
    4.0K        doc/design/figures
    4.0K        doc/design/modules
    20K doc/design/CMakeFiles/auxclean.dir
    44K doc/design/CMakeFiles/design_ps.dir
    20K doc/design/CMakeFiles/html.dir
    20K doc/design/CMakeFiles/design_html.dir
    20K doc/design/CMakeFiles/design.dir
    28K doc/design/CMakeFiles/design_auxclean.dir
    40K doc/design/CMakeFiles/design_dvi.dir
    20K doc/design/CMakeFiles/pdf.dir
    20K doc/design/CMakeFiles/safepdf.dir
    20K doc/design/CMakeFiles/ps.dir
    20K doc/design/CMakeFiles/design_safepdf.dir
    40K doc/design/CMakeFiles/design_pdf.dir
    20K doc/design/CMakeFiles/dvi.dir
    344K        doc/design/CMakeFiles
    4.0K        doc/design/other-chapters
    380K        doc/design
    12K doc/bin/CMakeFiles
    36K doc/bin
    8.0K        doc/imgs
    20K doc/CMakeFiles/update_mathjax.dir
    40K doc/CMakeFiles/doxysql.dir
    20K doc/CMakeFiles/devdoc.dir
    20K doc/CMakeFiles/doc.dir
    112K        doc/CMakeFiles
    12K doc/etc/CMakeFiles
    152K        doc/etc
    720K        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.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

Reply via email to