This package provides methods for computing distances between rows of general Tables.jl tables using the ecosystem of scientific types available in DataScienceTraits.jl. It follows the Distances.jl interface as much as possible.

A common task in statistics and machine learning consists of computing distances between observations for different purposes (e.g. clustering, kernel methods). When the data is homogeneous, i.e. all the attributes have the same scientific type, one can use packages such as Distances.jl directly on the result of Tables.matrix(table). On the other hand, when the table is heterogeneous, one must combine different distances for the various attributes using some weighting scheme.

Get the latest stable release with Julia's package manager:

] add TableDistances

We follow the Distances.jl interface as much as possible:

julia> using TableDistances
julia> table = (a=1:3, b=rand(3), c=["A", "B", "C"], d=[1, 2, 4])
(a = 1:3, b = [0.7596581938450753, 0.6952806574889876, 0.6669145844749085], c = ["A", "B", "C"], d = [1, 2, 4])
julia> D = pairwise(TableDistance(), table)
3×3 Matrix{Float64}:
 0.0      1.09707   1.25
 1.09707  0.0       0.902927
 1.25     0.902927  0.0

Contributions are very welcome. Please open an issue if you have questions.

• Júlio Hoffimann
• José Augusto