Chiwan Park created FLINK-1933: ---------------------------------- 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332)