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README.md

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# POT: Python Optimal Transport library
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# POT: Python Optimal Transport
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This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.
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It provides the following solvers:
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* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel [1]).
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* OT solver for the linear program/ Earth Movers Distance [1].
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* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2].
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* Bregman projections for Wasserstein barycenter [3] and unmixing [4].
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* Optimal transport for domain adaptation with group lasso regularization [5]
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* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
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Some demonstrations (both in Python and Jupyter Notebook Format) are available in the examples folder.
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Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
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## Installation
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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:
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- Numpy (>=1.11)
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- Scipy (>=0.17)
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To install the library, you can install it locally (after downloading it) on you machine using
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```
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python setup.py install --user
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```
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After a correct installation, you should be able to import the module without errors:
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```python
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import ot
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```
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Note that for easier acesss the module is name ot instead of pot.
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## Examples
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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.
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* [1D Optimal transport](examples/Demo_1D_OT.ipynb)
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* [1D optimal transport](examples/Demo_1D_OT.ipynb)
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## Acknowledgements
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The main developers of this library are:
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* Rémi Flamary
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* Nicolas Courty
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The contributors to this library are:
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* [Rémi Flamary](http://remi.flamary.com/)
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* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)
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* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/)
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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):
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* Gabriel Peyré (Wasserstein Barycenters in Matlab)
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* Nicolas Bonneel ( C++ code for EMD)
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* Antoine Rolet ( Mex file fro EMD )
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* Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda)
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* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab)
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* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD)
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* [Antoine Rolet](https://arolet.github.io/) ( Mex file for EMD )
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* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda)
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## References
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