Metadata-Version: 1.1
Name: POT
Version: 0.1.9
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/V0.1.9.tar.gz
Description: POT: Python Optimal Transport
        =============================
        
        |Documentation Status|
        
        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].
        -  Joint OT matix and mapping etsimation [8].
        
        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)
        -  Cython (>=0.23)
        -  Matplotlib (>=1.5)
        
        Under debian based linux the dependencies can be installed with
        
        ::
        
            sudo apt-get install python-numpy python-scipy python-matplotlib cython
        
        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:
        
        .. code:: 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. The full documentation is available on
        `Readthedocs <http://pot.readthedocs.io/>`__
        
        Here is a list of the Python notebooks if you want a quick look:
        
        -  `1D optimal
           transport <https://github.com/rflamary/POT/blob/master/examples/Demo_1D_OT.ipynb>`__
        -  `2D optimal transport on empirical
           distributions <https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb>`__
        -  `1D Wasserstein
           barycenter <https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb>`__
        -  `OT with user provided
           regularization <https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb>`__
        -  `Domain adaptation with optimal
           transport <https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_DomainAdaptation.ipynb>`__
        -  `Color transfer in
           images <https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb>`__
        -  `OT mapping estimation for domain
           adaptation <https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OTmapping_DomainAdaptation.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.
        
        [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation
        for discrete optimal transport", Neural Information Processing Systems
        (NIPS), 2016.
        
        .. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest
           :target: http://pot.readthedocs.io/en/latest/?badge=latest
        
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
Requires: scipy
Requires: cython
Requires: matplotlib
