Metadata-Version: 1.1
Name: POT
Version: 0.1.4
Summary: Python Optimal Transport Library
Home-page: https://github.com/rflamary/POT
Author: Remi Flamary, Nicolas Courty
Author-email: remi.flamary@gmail.com, ncourty@gmail.com
License: MIT
Download-URL: https://github.com/rflamary/POT/archive/V0.1.4.tar.gz
Description: # 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:
        * 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.
        
        ## 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
        ```
        
        The toolbox is also available on PyPI with a possibly slightly older version. You can install it with:
        ```
        pip install POT
        ```
        
        After a correct installation, you should be able to import the module without errors:
        ```python
        import ot
        ```
        
        Note that for easier access the module is name ot instead of pot.
        
        ## Examples
        
        The examples folder contain several examples and use case for the library.
        
         Here is a list of the Python notebook if you want a quick look:
        
        * [1D optimal transport](examples/Demo_1D_OT.ipynb)
        * [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb)
        * [1D Wasserstein barycenter](examples/Demo_1D_barycenter.ipynb)
        * [OT with user provided regularization](examples/Demo_Optim_OTreg.ipynb)
        
        
        ## Acknowledgements
        
        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](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
        
        [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
        
        [2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300).
        
        [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
        
        [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.
        
        [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
        
        [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
        
        [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
        
Platform: linux
Platform: macosx
Platform: windows
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Topic :: Utilities
Requires: numpy (>=1.11)
Requires: scipy (>=0.17)
Requires: cython (>=0.23)
