[jira] [Commented] (SPARK-3012) Standardized Distance Functions between two Vectors for MLlib

["Dhaval Modi (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3012?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15745388#comment-15745388 ] 

Dhaval Modi commented on SPARK-3012:
------------------------------------

I have implemented Mahalanobis Distance in Spark 1.6.2+ using Breeze 0.12 libraries. One can refer below scala code at:
https://github.com/dhmodi/MahalanobisDistance

> Standardized Distance Functions between two Vectors for MLlib
> -------------------------------------------------------------
>
>                 Key: SPARK-3012
>                 URL: https://issues.apache.org/jira/browse/SPARK-3012
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Yu Ishikawa
>            Priority: Minor
>
> Most of the clustering algorithms need distance functions between two Vectors.
> We should include the standardized distance function library in MLlib.
> I think that the standardized distance functions help us to implement more machine learning algorithms efficiently.
> h3. For example
> - Chebyshev Distance
> - Cosine Distance
> - Euclidean Distance
> - Mahalanobis Distance
> - Manhattan Distance
> - Minkowski Distance
> - SquaredEuclidean Distance
> - Tanimoto Distance
> - Weighted Distance
> - WeightedEuclidean Distance
> - WeightedManhattan Distance



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