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author | Rémi Flamary <remi.flamary@gmail.com> | 2016-10-27 16:53:29 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2016-10-27 16:53:29 +0200 |
commit | 8c88f7065c240c424d5bab80a813aa6e7ff879d1 (patch) | |
tree | f72ee82d703d7c866e3610b85f3d5c609fa631ba /README.md | |
parent | cb187e2cf8316ee7fe9a65f1d4381fb8baf8050f (diff) |
readme with references
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-rw-r--r-- | README.md | 42 |
1 files changed, 30 insertions, 12 deletions
@@ -1,39 +1,57 @@ -# POT: Python Optimal Transport library +# POT: Python Optimal Transport This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel [1]). +* OT solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -Some demonstrations (both in Python and Jupyter Notebook Format) are available in the examples folder. - +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. ## Installation +The Library has been tested on Linux and MacOSX. It requires a C++ compiler for using the EMD solver and rely on the following Python modules: + +- Numpy (>=1.11) +- Scipy (>=0.17) + +To install the library, you can install it locally (after downloading it) on you machine using +``` +python setup.py install --user +``` + + + +After a correct installation, you should be able to import the module without errors: +```python +import ot +``` + +Note that for easier acesss the module is name ot instead of pot. ## Examples The examples folder contain several examples abnd use case for the library. Here is a list of the Ypython notebook if you want a quick look. -* [1D Optimal transport](examples/Demo_1D_OT.ipynb) +* [1D optimal transport](examples/Demo_1D_OT.ipynb) ## Acknowledgements -The main developers of this library are: -* Rémi Flamary -* Nicolas Courty +The contributors to this library are: +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) +* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) 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): -* Gabriel Peyré (Wasserstein Barycenters in Matlab) -* Nicolas Bonneel ( C++ code for EMD) -* Antoine Rolet ( Mex file fro EMD ) -* Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) +* [Antoine Rolet](https://arolet.github.io/) ( Mex file for EMD ) +* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) ## References |