[jira] [Commented] (MADLIB-927) Initial implementation of k-NN

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15813020#comment-15813020 ] 

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

Github user orhankislal commented on the issue:

    https://github.com/apache/incubator-madlib/pull/81
  
    When I run the install-check I get the following error. 
    ```
    select * from madlib.knn('knn_train_data','data','label','knn_test_data','data','id','madlib_knn_result_classification','c',3);
    psql:/tmp/madlib.vuBBXN/knn/test/knn.sql_in.tmp:137: ERROR:  mode is not an ordered-set aggregate, so it cannot have WITHIN GROUP
    LINE 3:  select test_id as id, data, mode() within group(order by la...
                                         ^
    QUERY:  
    	CREATE TABLE madlib_knn_result_classification AS
    	select test_id as id, data, mode() within group(order by label) as predLabel from pg_temp.madlib_knn_interm join knn_test_data  on test_id=id group by test_id, data
    CONTEXT:  PL/pgSQL function knn(character varying,character varying,character varying,character varying,character varying,character varying,character varying,character varying,integer) line 44 at EXECUTE statement
    ```
    Have you encountered this before? I am using postgres 9.4.


> 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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