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