~ruther/guix-local

f8e9462982341d6a67af79b5d99e3163a8b11ce1 — Ricardo Wurmus 1 year, 2 months ago d53a56f
gnu: Add python-geomloss.

* gnu/packages/machine-learning.scm (python-geomloss): New variable.

Change-Id: Id54d81c8c942c69151a7667983073a28419170d0
1 files changed, 31 insertions(+), 0 deletions(-)

M gnu/packages/machine-learning.scm
M gnu/packages/machine-learning.scm => gnu/packages/machine-learning.scm +31 -0
@@ 5530,6 5530,37 @@ and common image transformations for computer vision.")
Python.")
    (license license:bsd-3)))

(define-public python-geomloss
  (package
    (name "python-geomloss")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (pypi-uri "geomloss" version))
       (sha256
        (base32 "1szsjpcwjlvqiiws120fwn581a6hs8gm9si8c75v40ahbh44f729"))))
    (build-system pyproject-build-system)
    ;; There are no automated tests.
    (arguments (list #:tests? #false))
    (propagated-inputs (list python-numpy python-pytorch))
    (native-inputs (list python-setuptools python-wheel))
    (home-page "https://www.kernel-operations.io/geomloss/")
    (synopsis
     "Geometric loss functions between point clouds, images and volumes")
    (description
     "The GeomLoss library provides efficient GPU implementations for:

@itemize
@item Kernel norms (also known as Maximum Mean Discrepancies).
@item Hausdorff divergences, which are positive definite generalizations of
the Chamfer-ICP loss and are analogous to log-likelihoods of Gaussian Mixture
Models.
@item Debiased Sinkhorn divergences, which are affordable yet positive and
definite approximations of Optimal Transport (Wasserstein) distances.
@end itemize")
    (license license:expat)))

(define-public python-hmmlearn
  (package
    (name "python-hmmlearn")