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author | Rémi Flamary <remi.flamary@gmail.com> | 2020-04-20 22:04:03 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2020-04-20 22:04:03 +0200 |
commit | 21949bbc3469234f88972bdfe973f68eb9e62794 (patch) | |
tree | 6bc93db587bd80d0ccb9e33596c4526aeaefec4c /README.md | |
parent | d54184c233cd211a693e4cdf4b25dd68b07ed00b (diff) | |
parent | 43b2190db71b1ccbeec8fddaae23ca6af220e1b5 (diff) |
Merge branch 'master' into doc_travis
Diffstat (limited to 'README.md')
-rw-r--r-- | README.md | 45 |
1 files changed, 24 insertions, 21 deletions
@@ -2,11 +2,11 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) +[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) -[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) +[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/PythonOT/POT/blob/master/LICENSE) @@ -20,7 +20,7 @@ It provides the following solvers: * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] +* Optimal transport for domain adaptation with group lasso regularization and Laplacian regularization [5][30] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Linear OT [14] and Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). @@ -140,26 +140,26 @@ ba=ot.barycenter(A,M,reg) # reg is regularization parameter The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). -Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: +Here is a list of the Python notebooks available [here](https://github.com/PythonOT/POT/blob/master/notebooks/) if you want a quick look: -* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) -* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) -* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) -* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) +* [1D optimal transport](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_1D.ipynb) +* [OT Ground Loss](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https://github.com/PythonOT/POT/blob/master/notebooks/plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https://github.com/PythonOT/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_d2.ipynb) +* [Color transfer in images](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/PythonOT/POT/blob/master/notebooks/plot_WDA.ipynb) +* [Gromov Wasserstein](https://github.com/PythonOT/POT/blob/master/notebooks/plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https://github.com/PythonOT/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/PythonOT/POT/tree/master/notebooks/). ## Acknowledgements @@ -184,6 +184,7 @@ The contributors to this library are * [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) * [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) * [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) +* [Ievgen Redko](https://ievred.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -264,4 +265,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276.
\ No newline at end of file +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. + +[30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. |