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author | Rémi Flamary <remi.flamary@gmail.com> | 2020-04-22 12:44:45 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2020-04-22 12:44:45 +0200 |
commit | bffba0033fda3a45d7cbbde5165e09e886262ab2 (patch) | |
tree | 93109856a86073db8c8e08ea089dc21d5955033b | |
parent | 20da1f630dac2639ae86f625b46d4270e384f351 (diff) |
awesome new readme
-rw-r--r-- | README.md | 52 | ||||
-rw-r--r-- | docs/source/readme.rst | 111 |
2 files changed, 117 insertions, 46 deletions
@@ -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} } ``` diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 76d37a4..4862523 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -10,31 +10,84 @@ machine learning. Website and documentation: 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]. \* 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] -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]. +Source Code (MIT): 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 <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 <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>`__. @@ -47,10 +100,16 @@ 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} } |