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https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15819751#comment-15819751
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ASF GitHub Bot commented on MADLIB-927:
---------------------------------------
Github user auonhaidar commented on the issue:
https://github.com/apache/incubator-madlib/pull/81
Output:
-- The C compiler identification is GNU 4.8.4
-- The CXX compiler identification is GNU 4.8.4
-- Check for working C compiler: /usr/bin/gcc
-- Check for working C compiler: /usr/bin/gcc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/g++
-- Check for working CXX compiler: /usr/bin/g++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Boost version: 1.60.0
-- Found PythonInterp: /usr/bin/python (found version "2.7.6")
-- Found PostgreSQL: /usr/local/pgsql/bin/postgres
-- Found PostgreSQL_9_4: /usr/local/pgsql/bin/postgres
>> Adding PostgreSQL 9.4 (x86_64) to target list...
-- Could NOT find Greenplum (missing: GREENPLUM_EXECUTABLE)
-- Could NOT find HAWQ (missing: HAWQ_EXECUTABLE)
-- Found FLEX: /usr/bin/flex (found suitable version "2.5.35", minimum
required is "2.5.33")
-- Found BISON: /usr/bin/bison (found suitable version "3.0.2", minimum
required is "2.4")
-- Found Doxygen: /usr/local/bin/doxygen (found version "1.8.14")
-- Configuring done
-- Generating done
-- Build files have been written to:
/home/haidar/incubator-madlib-e1c99c1/build
> 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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