From 11c2c26ff897e5763e714546e7021cffa8d673a7 Mon Sep 17 00:00:00 2001 From: tvayer Date: Tue, 28 May 2019 17:19:40 +0200 Subject: solve 2 --- README.md | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) (limited to 'README.md') diff --git a/README.md b/README.md index 13e1013..9951773 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ [![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) [![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) @@ -14,15 +15,18 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. * 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] and unmixing [4]. +* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * 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). * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized free support Wasserstein barycenters [20]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -77,16 +81,12 @@ Note that for easier access the module is name ot instead of pot. Some sub-modules require additional dependences which are discussed below -* **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: +* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be installed with: -``` -git clone https://github.com/cudamat/cudamat.git -cd cudamat -python setup.py install --user # for user install (no root) -``` +* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). + obviously you need CUDA installed and a compatible GPU. @@ -162,6 +162,8 @@ The contributors to this library are: * [Stanislas Chambon](https://slasnista.github.io/) * [Antoine Rolet](https://arolet.github.io/) * Erwan Vautier (Gromov-Wasserstein) +* [Kilian Fatras](https://kilianfatras.github.io/) +* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) 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): -- cgit v1.2.3