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Merge pull request #160 from PythonOT/doc_cov
[MRG] Update documentation and improve coverage
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‎.github/workflows/build_wheels.yml‎

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on:
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release:
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push:
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branches:
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- "master"
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jobs:
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build_wheels:

‎Makefile‎

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rdoc :
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pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md
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sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/readme.rst
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pandoc --from=markdown --to=rst --output=docs/source/releases.rst RELEASES.md
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sed -i 's,https://pot.readthedocs.io/en/latest/,,g' docs/source/releases.rst
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sed -i 's,https://github.com/rflamary/POT/blob/master/notebooks/,auto_examples/,g' docs/source/releases.rst
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sed -i 's,.ipynb,.html,g' docs/source/releases.rst
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sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/releases.rst
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notebook :
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ipython notebook --matplotlib=inline --notebook-dir=notebooks/

‎README.md‎

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* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
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* [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].
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* 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).
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* 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].
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* 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].
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* Sinkhorn divergence [23] and entropic regularization OT from empirical data.
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* [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].
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* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
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* [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])
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* [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]
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* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
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* [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])
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* [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]
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* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
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* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html) [20].
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* [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].
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* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
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* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
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* [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].
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* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
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POT provides the following Machine Learning related solvers:
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* [Optimal transport for domain
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adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html)
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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
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supervised setting](https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html).
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* [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].
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* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/plot_WDA.html) [11] (requires autograd + pymanopt).
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* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/plot_otda_jcpot.html) [27].
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adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html)
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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
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supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html).
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* [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/domain-adaptation/plot_otda_mapping.html) [8].
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* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt).
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* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27].
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Some demonstrations are available in the [documentation](https://pythonot.github.io/auto_examples/index.html).
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Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html).
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#### Using and citing the toolbox
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### Examples and Notebooks
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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/).
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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/).
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## Acknowledgements

‎RELEASES.md‎

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# POT Releases
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# Releases
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## 0.6 Year 3
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## 0.6
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## 0.5.0 Year 2
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## 0.5.0
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## 0.4 Community edition
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## 0.4
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## 0.3 Summer release
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## 0.3
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## V0.1.11 New years resolution
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## 0.1.11
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## 0.1.10
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## V0.1.9 DA classes and mapping
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## 0.1.9
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## 0.1.7
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‎docs/source/_templates/module.rst‎

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{{ fullname }}
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{% block functions %}
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Functions
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{% for item in functions %}
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<div class="sphx-glr-clear"></div>
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{% block classes %}
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‎docs/source/all.rst‎

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.. _sphx_glr_api_reference:
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Python modules
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API and modules
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===============
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ot
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ot.lp
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