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https://issues.apache.org/jira/browse/FLINK-1933?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14526401#comment-14526401
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ASF GitHub Bot commented on FLINK-1933:
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Github user tillrohrmann commented on the pull request:
https://github.com/apache/flink/pull/629#issuecomment-98626668
Well done @chiwanpark.
The only thing which is missing is an overview page with all supported
distance measures for the Flink website. Could you add such a site? You find in
`flink/docs/libs/ml/` examples for other ML documentation. If you write 2-3
sentences and list all the different distance measures, then it would be
perfect :-) I will then merge the PR.
> Add distance measure interface and basic implementation to machine learning
> library
> -----------------------------------------------------------------------------------
>
> Key: FLINK-1933
> URL: https://issues.apache.org/jira/browse/FLINK-1933
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Chiwan Park
> Assignee: Chiwan Park
> Labels: ML
>
> Add distance measure interface to calculate distance between two vectors and
> some implementations of the interface. In FLINK-1745, [~till.rohrmann]
> suggests a interface following:
> {code}
> trait DistanceMeasure {
> def distance(a: Vector, b: Vector): Double
> }
> {code}
> I think that following list of implementation is sufficient to provide first
> to ML library users.
> * Manhattan distance [1]
> * Cosine distance [2]
> * Euclidean distance (and Squared) [3]
> * Tanimoto distance [4]
> * Minkowski distance [5]
> * Chebyshev distance [6]
> [1]: http://en.wikipedia.org/wiki/Taxicab_geometry
> [2]: http://en.wikipedia.org/wiki/Cosine_similarity
> [3]: http://en.wikipedia.org/wiki/Euclidean_distance
> [4]:
> http://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_coefficient_.28extended_Jaccard_coefficient.29
> [5]: http://en.wikipedia.org/wiki/Minkowski_distance
> [6]: http://en.wikipedia.org/wiki/Chebyshev_distance
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