From bffba0033fda3a45d7cbbde5165e09e886262ab2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 12:44:45 +0200 Subject: awesome new readme --- README.md | 52 ++++++++++++++++++++++++++++++++-------------------- 1 file changed, 32 insertions(+), 20 deletions(-) (limited to 'README.md') diff --git a/README.md b/README.md index dff334e..ad0d810 100644 --- a/README.md +++ b/README.md @@ -16,39 +16,51 @@ learning. Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/) -POT provides the following generic OT solvers: -* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], - stabilized version [9] [10], greedy Sinkhorn [22] and Screening Sinkhorn [26] with optional GPU - implementation (requires cupy). -* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. +Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) + +POT provides the following generic OT solvers (links to examples): + +* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] . +* [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7]. +* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy). +* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html) [21] and unmixing [4]. * 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). -* 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]. -* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] +* [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17]. +* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale). +* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html) (exact [13] and regularized [12]) + * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html) [24] +* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html) [20]. +* [Unbalanced OT](https://pythonot.github.io/auto_examples/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html) [10, 25]. +* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3] formulations). POT provides the following Machine Learning related solvers: -* Optimal transport for domain adaptation with group lasso regularization and Laplacian regularization [5] [30]. -* Linear OT [14] and Joint OT matrix and mapping estimation [8]. -* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -* JCPOT algorithm for multi-source domain adaptation with target shift [27]. + +* [Optimal transport for domain + adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html) + with [group lasso regularization](https://pythonot.github.io/auto_examples/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/plot_otda_laplacian.html) [5] [30] and [semi + supervised setting](https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html). +* [Linear OT mapping](https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8]. +* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/plot_WDA.html) [11] (requires autograd + pymanopt). +* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/plot_otda_jcpot.html) [27]. Some demonstrations are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). #### Using and citing the toolbox -If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: +If you use this toolbox in your research and find it useful, please cite POT +using the following bibtex reference: +``` +Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017 +``` + +In Bibtex format: ``` @misc{flamary2017pot, title={POT Python Optimal Transport library}, author={Flamary, R{'e}mi and Courty, Nicolas}, -url={https://github.com/rflamary/POT}, +url={https://pythonot.github.io/}, year={2017} } ``` -- cgit v1.2.3