From 71e79844a54ea88babd04b01688766a17b3de614 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Fri, 24 Apr 2020 18:25:17 +0200
Subject: update proper links form readme

---
 docs/source/index.rst  |  3 ++-
 docs/source/readme.rst | 38 +++++++++++++++++++-------------------
 2 files changed, 21 insertions(+), 20 deletions(-)

(limited to 'docs')

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
-- 
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