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ASF GitHub Bot commented on FLINK-1933: --------------------------------------- Github user tillrohrmann commented on a diff in the pull request: https://github.com/apache/flink/pull/629#discussion_r29320567 --- Diff: flink-staging/flink-ml/src/test/scala/org/apache/flink/ml/math/SparseVectorSuite.scala --- @@ -87,4 +87,33 @@ class SparseVectorSuite extends FlatSpec with Matchers { sparseVector should not equal(copy) } + + it should "calculate dot product with SparseVector" in { + val vec1 = SparseVector.fromCOO(4, (0, 1), (1, 0), (2, 1), (3, 0)) + val vec2 = SparseVector.fromCOO(4, (0, 0), (1, 1), (2, 0), (3, 1)) --- End diff -- Again non-zero entries are continuous starting from index 0. We should add a test case which works on more irregular non-zero entries. > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332)