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-rw-r--r-- | README.md | 28 | ||||
-rw-r--r-- | docs/source/index.rst | 3 | ||||
-rw-r--r-- | docs/source/readme.rst | 38 |
3 files changed, 35 insertions, 34 deletions
@@ -22,29 +22,29 @@ 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]. +* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/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] +* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale). +* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12]) + * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/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] +* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20]. +* [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25]. +* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/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]. + with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi + supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html). +* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/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/domain-adaptation/plot_WDA.html) [11] (requires autograd + pymanopt). +* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27]. -Some demonstrations are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). +Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). #### Using and citing the toolbox @@ -153,7 +153,7 @@ ba=ot.barycenter(A,M,reg) # reg is regularization parameter ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [https://PythonOT.github.io/](https://PythonOT.github.io/). +The examples folder contain several examples and use case for the library. The full documentation with examples and output is available on [https://PythonOT.github.io/](https://PythonOT.github.io/). ## Acknowledgements diff --git a/docs/source/index.rst b/docs/source/index.rst index 47a29a4..be01343 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -19,7 +19,8 @@ Contents releases .. include:: readme.rst - :start-line: 5 + :start-line: 2 + Indices and tables diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 7d8b8bd..2707a07 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -29,9 +29,9 @@ POT provides the following generic OT solvers (links to examples): [26] <auto_examples/plot_screenkhorn_1D.html>`__ with optional GPU implementation (requires cupy). - Bregman projections for `Wasserstein - barycenter <auto_examples/plot_barycenter_lp_vs_entropic.html>`__ + barycenter <auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html>`__ [3], `convolutional - barycenter <auto_examples/plot_convolutional_barycenter.html>`__ + barycenter <auto_examples/barycenters/plot_convolutional_barycenter.html>`__ [21] and unmixing [4]. - Sinkhorn divergence [23] and entropic regularization OT from empirical data. @@ -39,32 +39,32 @@ POT provides the following generic OT solvers (links to examples): solvers <auto_examples/plot_OT_1D_smooth.html>`__ (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized `Wasserstein barycenters - [16] <auto_examples/plot_barycenter_lp_vs_entropic.html>`__) + [16] <auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html>`__) with LP solver (only small scale). - `Gromov-Wasserstein - distances <auto_examples/plot_gromov.html>`__ + distances <auto_examples/gromov/plot_gromov.html>`__ and `GW - barycenters <auto_examples/plot_gromov_barycenter.html>`__ + barycenters <auto_examples/gromov/plot_gromov_barycenter.html>`__ (exact [13] and regularized [12]) - `Fused-Gromov-Wasserstein distances - solver <auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__ + solver <auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__ and `FGW - barycenters <auto_examples/plot_barycenter_fgw.html>`__ + barycenters <auto_examples/gromov/plot_barycenter_fgw.html>`__ [24] - `Stochastic solver <auto_examples/plot_stochastic.html>`__ for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) - Non regularized `free support Wasserstein - barycenters <auto_examples/plot_free_support_barycenter.html>`__ + barycenters <auto_examples/barycenters/plot_free_support_barycenter.html>`__ [20]. - `Unbalanced - OT <auto_examples/plot_UOT_1D.html>`__ + OT <auto_examples/unbalanced-partial/plot_UOT_1D.html>`__ with KL relaxation and - `barycenter <auto_examples/plot_UOT_barycenter_1D.html>`__ + `barycenter <auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html>`__ [10, 25]. - `Partial Wasserstein and - Gromov-Wasserstein <auto_examples/plot_partial_wass_and_gromov.html>`__ + Gromov-Wasserstein <auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html>`__ (exact [29] and entropic [3] formulations). POT provides the following Machine Learning related solvers: @@ -72,24 +72,24 @@ POT provides the following Machine Learning related solvers: - `Optimal transport for domain adaptation <auto_examples/plot_otda_classes.html>`__ with `group lasso - regularization <auto_examples/plot_otda_classes.html>`__, + regularization <auto_examples/domain-adaptation/plot_otda_classes.html>`__, `Laplacian - regularization <auto_examples/plot_otda_laplacian.html>`__ + regularization <auto_examples/domain-adaptation/plot_otda_laplacian.html>`__ [5] [30] and `semi supervised - setting <auto_examples/plot_otda_semi_supervised.html>`__. + setting <auto_examples/domain-adaptation/plot_otda_semi_supervised.html>`__. - `Linear OT - mapping <auto_examples/plot_otda_linear_mapping.html>`__ + mapping <auto_examples/domain-adaptation/plot_otda_linear_mapping.html>`__ [14] and `Joint OT mapping estimation <auto_examples/plot_otda_mapping.html>`__ [8]. - `Wasserstein Discriminant - Analysis <auto_examples/plot_WDA.html>`__ + Analysis <auto_examples/domain-adaptation/plot_WDA.html>`__ [11] (requires autograd + pymanopt). - `JCPOT algorithm for multi-source domain adaptation with target - shift <auto_examples/plot_otda_jcpot.html>`__ + shift <auto_examples/domain-adaptation/plot_otda_jcpot.html>`__ [27]. -Some demonstrations are available in the +Some other examples are available in the `documentation <auto_examples/index.html>`__. Using and citing the toolbox @@ -233,7 +233,7 @@ Examples and Notebooks ~~~~~~~~~~~~~~~~~~~~~~ The examples folder contain several examples and use case for the -library. The full documentation is available on +library. The full documentation with examples and output is available on https://PythonOT.github.io/. Acknowledgements |