[jira] [Created] (FLINK-1933) Add distance measure interface and basic implementation to machine learning library

["Chiwan Park (JIRA)" <ji...@apache.org>]

Chiwan Park created FLINK-1933:
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             Summary: 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


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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