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diff --git a/docs/source/readme.rst b/docs/source/readme.rst index b00d7b7..c96f191 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -1,8 +1,8 @@ POT: Python Optimal Transport ============================= -|PyPI version| |Anaconda Cloud| |Build Status| |Build Status| |Codecov -Status| |Downloads| |Anaconda downloads| |License| +|PyPI version| |Anaconda Cloud| |Build Status| |Codecov Status| +|Downloads| |Anaconda downloads| |License| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -29,9 +29,9 @@ POT provides the following generic OT solvers (links to examples): [26] <auto_examples/plot_screenkhorn_1D.html>`__ with optional GPU implementation (requires cupy). - Bregman projections for `Wasserstein - barycenter <auto_examples/plot_barycenter_lp_vs_entropic.html>`__ + barycenter <auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html>`__ [3], `convolutional - barycenter <auto_examples/plot_convolutional_barycenter.html>`__ + barycenter <auto_examples/barycenters/plot_convolutional_barycenter.html>`__ [21] and unmixing [4]. - Sinkhorn divergence [23] and entropic regularization OT from empirical data. @@ -39,68 +39,69 @@ POT provides the following generic OT solvers (links to examples): solvers <auto_examples/plot_OT_1D_smooth.html>`__ (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized `Wasserstein barycenters - [16] <auto_examples/plot_barycenter_lp_vs_entropic.html>`__) + [16] <auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html>`__) with LP solver (only small scale). - `Gromov-Wasserstein - distances <auto_examples/plot_gromov.html>`__ + distances <auto_examples/gromov/plot_gromov.html>`__ and `GW - barycenters <auto_examples/plot_gromov_barycenter.html>`__ + barycenters <auto_examples/gromov/plot_gromov_barycenter.html>`__ (exact [13] and regularized [12]) - `Fused-Gromov-Wasserstein distances - solver <auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__ + solver <auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__ and `FGW - barycenters <auto_examples/plot_barycenter_fgw.html>`__ + barycenters <auto_examples/gromov/plot_barycenter_fgw.html>`__ [24] - `Stochastic solver <auto_examples/plot_stochastic.html>`__ for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) - Non regularized `free support Wasserstein - barycenters <auto_examples/plot_free_support_barycenter.html>`__ + barycenters <auto_examples/barycenters/plot_free_support_barycenter.html>`__ [20]. - `Unbalanced - OT <auto_examples/plot_UOT_1D.html>`__ + OT <auto_examples/unbalanced-partial/plot_UOT_1D.html>`__ with KL relaxation and - `barycenter <auto_examples/plot_UOT_barycenter_1D.html>`__ + `barycenter <auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html>`__ [10, 25]. - `Partial Wasserstein and - Gromov-Wasserstein <auto_examples/plot_partial_wass_and_gromov.html>`__ + Gromov-Wasserstein <auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html>`__ (exact [29] and entropic [3] formulations). POT provides the following Machine Learning related solvers: - `Optimal transport for domain - adaptation <auto_examples/plot_otda_classes.html>`__ + adaptation <auto_examples/domain-adaptation/plot_otda_classes.html>`__ with `group lasso - regularization <auto_examples/plot_otda_classes.html>`__, + regularization <auto_examples/domain-adaptation/plot_otda_classes.html>`__, `Laplacian - regularization <auto_examples/plot_otda_laplacian.html>`__ + regularization <auto_examples/domain-adaptation/plot_otda_laplacian.html>`__ [5] [30] and `semi supervised - setting <auto_examples/plot_otda_semi_supervised.html>`__. + setting <auto_examples/domain-adaptation/plot_otda_semi_supervised.html>`__. - `Linear OT - mapping <auto_examples/plot_otda_linear_mapping.html>`__ + mapping <auto_examples/domain-adaptation/plot_otda_linear_mapping.html>`__ [14] and `Joint OT mapping - estimation <auto_examples/plot_otda_mapping.html>`__ + estimation <auto_examples/domain-adaptation/plot_otda_mapping.html>`__ [8]. - `Wasserstein Discriminant - Analysis <auto_examples/plot_WDA.html>`__ + Analysis <auto_examples/others/plot_WDA.html>`__ [11] (requires autograd + pymanopt). - `JCPOT algorithm for multi-source domain adaptation with target - shift <auto_examples/plot_otda_jcpot.html>`__ + shift <auto_examples/domain-adaptation/plot_otda_jcpot.html>`__ [27]. -Some demonstrations are available in the +Some other examples are available in the `documentation <auto_examples/index.html>`__. Using and citing the toolbox ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you use this toolbox in your research and find it useful, please cite -POT using the following bibtex reference: +POT using the following reference: :: - Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017 + Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, + Website: https://pythonot.github.io/, 2017 In Bibtex format: @@ -141,11 +142,11 @@ You can install the toolbox through PyPI with: pip install POT -or get the very latest version by downloading it and then running: +or get the very latest version by running: :: - python setup.py install --user # for user install (no root) + pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root) Anaconda installation with conda-forge ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -232,7 +233,7 @@ Examples and Notebooks ~~~~~~~~~~~~~~~~~~~~~~ The examples folder contain several examples and use case for the -library. The full documentation is available on +library. The full documentation with examples and output is available on https://PythonOT.github.io/. Acknowledgements @@ -245,7 +246,8 @@ This toolbox has been created and is maintained by The contributors to this library are -- `Alexandre Gramfort <http://alexandre.gramfort.net/>`__ (CI) +- `Alexandre Gramfort <http://alexandre.gramfort.net/>`__ (CI, + documentation) - `Laetitia Chapel <http://people.irisa.fr/Laetitia.Chapel/>`__ (Partial OT) - `Michael Perrot <http://perso.univ-st-etienne.fr/pem82055/>`__ @@ -452,8 +454,6 @@ NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg :target: https://anaconda.org/conda-forge/pot -.. |Build Status| image:: https://travis-ci.org/PythonOT/POT.svg?branch=master - :target: https://travis-ci.org/PythonOT/POT .. |Build Status| image:: https://github.com/PythonOT/POT/workflows/build/badge.svg :target: https://github.com/PythonOT/POT/actions .. |Codecov Status| image:: https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg |