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| author | Marc Glisse <marc.glisse@inria.fr> | 2020-02-12 12:44:51 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2020-02-12 12:44:51 +0100 |
| commit | bed30b19e57669c0b8ad385f1124586ed3499a2d (patch) | |
| tree | 6ddbe2f3015899159818bd6e3003d78dd920d707 /src/python/doc | |
| parent | ee0f12f1df406c81c6ad860c494eed908021fad9 (diff) | |
| parent | d6f3165831d20bf3a91f1ff7e9734a574eaa567a (diff) | |
Merge pull request #182 from mglisse/ext
Interface to hera's Wasserstein distance
Diffstat (limited to 'src/python/doc')
| -rw-r--r-- | src/python/doc/installation.rst | 5 | ||||
| -rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 17 |
2 files changed, 16 insertions, 6 deletions
diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 40f3f44b..d459145b 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -14,10 +14,11 @@ Compiling ********* The library uses c++14 and requires `Boost <https://www.boost.org/>`_ ≥ 1.56.0, `CMake <https://www.cmake.org/>`_ ≥ 3.1 to generate makefiles, -`NumPy <http://numpy.org>`_ and `Cython <https://www.cython.org/>`_ to compile +`NumPy <http://numpy.org>`_, `Cython <https://www.cython.org/>`_ and +`pybind11 <https://github.com/pybind/pybind11>`_ to compile the GUDHI Python module. It is a multi-platform library and compiles on Linux, Mac OSX and Visual -Studio 2015. +Studio 2017. On `Windows <https://wiki.python.org/moin/WindowsCompilers>`_ , only Python ≥ 3.5 are available because of the required Visual Studio version. diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 32999a0c..94b454e2 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -9,17 +9,26 @@ Definition .. include:: wasserstein_distance_sum.inc -This implementation is based on ideas from "Large Scale Computation of Means and Cluster for Persistence Diagrams via Optimal Transport". +Functions +--------- +This implementation uses the Python Optimal Transport library and is based on +ideas from "Large Scale Computation of Means and Cluster for Persistence +Diagrams via Optimal Transport" :cite:`10.5555/3327546.3327645`. -Function --------- .. autofunction:: gudhi.wasserstein.wasserstein_distance +This other implementation comes from `Hera +<https://bitbucket.org/grey_narn/hera/src/master/>`_ (BSD-3-Clause) which is +based on "Geometry Helps to Compare Persistence Diagrams" +:cite:`Kerber:2017:GHC:3047249.3064175` by Michael Kerber, Dmitriy +Morozov, and Arnur Nigmetov. + +.. autofunction:: gudhi.hera.wasserstein_distance Basic example ------------- -This example computes the 1-Wasserstein distance from 2 persistence diagrams with euclidean ground metric. +This example computes the 1-Wasserstein distance from 2 persistence diagrams with Euclidean ground metric. Note that persistence diagrams must be submitted as (n x 2) numpy arrays and must not contain inf values. .. testcode:: |