Age | Commit message (Collapse) | Author |
|
Integration of Wasserstein distances in representations module
|
|
Automatic differentiation for Wasserstein distance
|
|
|
|
wasserstein_representations
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
DTM
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
eagerpy is only used with enable_autodiff
|
|
Still limited to L^p
|
|
|
|
|
|
This doesn't seem like the best way to handle it, we may want to handle
it like a wrapper that gets the indices from knn (whatever backend) and
then computes the distances.
|
|
It is supposed to be possible to compile numpy with openmp, but it looks
like it isn't done in any of the usual packages.
It may be possible to refactor that code so there is less redundancy.
|
|
|
|
|
|
|
|
|
|
Also simplify references, and replace print with assert for errors
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
modified index to remove barycenter doc
|
|
|
|
gudhi.wasserstein.barycenter
|
|
|
|
Extended persistence
|