update proper links form readme

Author: rflamary

README.md

@@ -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

docs/source/index.rst

@@ -19,7 +19,8 @@ Contents
    releases
 
 .. include:: readme.rst
-    :start-line: 5
+   :start-line: 2
+
 
 
 Indices and tables

docs/source/readme.rst

@@ -29,67 +29,67 @@ 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.
 -  `Smooth optimal transport
    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:
 
 -  `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