update doc

Author: rflamary

docs/source/readme.rst

@@ -39,6 +39,8 @@ It provides the following solvers:
 -  Screening Sinkhorn Algorithm for OT [26].
 -  JCPOT algorithm for multi-source domain adaptation with target shift
    [27].
+-  Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic
+   [3] formulations).
 
 Some demonstrations (both in Python and Jupyter Notebook format) are
 available in the examples folder.
@@ -51,12 +53,12 @@ POT using the following bibtex reference:
 
 ::
 
-   @misc{flamary2017pot,
-   title={POT Python Optimal Transport library},
-   author={Flamary, R{'e}mi and Courty, Nicolas},
-   url={https://github.com/rflamary/POT},
-   year={2017}
-   }
+    @misc{flamary2017pot,
+    title={POT Python Optimal Transport library},
+    author={Flamary, R{'e}mi and Courty, Nicolas},
+    url={https://github.com/rflamary/POT},
+    year={2017}
+    }
 
 Installation
 ------------
@@ -78,19 +80,19 @@ be installed prior to installing POT. This can be done easily with
 
 ::
 
-   pip install numpy cython
+    pip install numpy cython
 
 You can install the toolbox through PyPI with:
 
 ::
 
-   pip install POT
+    pip install POT
 
 or get the very latest version by downloading it and then running:
 
 ::
 
-   python setup.py install --user # for user install (no root)
+    python setup.py install --user # for user install (no root)
 
 Anaconda installation with conda-forge
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -101,7 +103,7 @@ required dependencies:
 
 ::
 
-   conda install -c conda-forge pot
+    conda install -c conda-forge pot
 
 Post installation check
 ^^^^^^^^^^^^^^^^^^^^^^^
@@ -111,7 +113,7 @@ without errors:
 
 .. code:: python
 
-   import ot
+    import ot
 
 Note that for easier access the module is name ot instead of pot.
 
@@ -124,9 +126,9 @@ below
 -  **ot.dr** (Wasserstein dimensionality reduction) depends on autograd
    and pymanopt that can be installed with:
 
-::
+   ::
 
-   pip install pymanopt autograd
+       pip install pymanopt autograd
 
 -  **ot.gpu** (GPU accelerated OT) depends on cupy that have to be
    installed following instructions on `this
@@ -142,36 +144,36 @@ Short examples
 
 -  Import the toolbox
 
-.. code:: python
+   .. code:: python
 
-   import ot
+       import ot
 
 -  Compute Wasserstein distances
 
-.. code:: python
+   .. code:: python
 
-   # a,b are 1D histograms (sum to 1 and positive)
-   # M is the ground cost matrix
-   Wd=ot.emd2(a,b,M) # exact linear program
-   Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT
-   # if b is a matrix compute all distances to a and return a vector
+       # a,b are 1D histograms (sum to 1 and positive)
+       # M is the ground cost matrix
+       Wd=ot.emd2(a,b,M) # exact linear program
+       Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT
+       # if b is a matrix compute all distances to a and return a vector
 
 -  Compute OT matrix
 
-.. code:: python
+   .. code:: python
 
-   # a,b are 1D histograms (sum to 1 and positive)
-   # M is the ground cost matrix
-   T=ot.emd(a,b,M) # exact linear program
-   T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT
+       # a,b are 1D histograms (sum to 1 and positive)
+       # M is the ground cost matrix
+       T=ot.emd(a,b,M) # exact linear program
+       T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT
 
 -  Compute Wasserstein barycenter
 
-.. code:: python
+   .. code:: python
 
-   # A is a n*d matrix containing d  1D histograms
-   # M is the ground cost matrix
-   ba=ot.barycenter(A,M,reg) # reg is regularization parameter
+       # A is a n*d matrix containing d  1D histograms
+       # M is the ground cost matrix
+       ba=ot.barycenter(A,M,reg) # reg is regularization parameter
 
 Examples and Notebooks
 ~~~~~~~~~~~~~~~~~~~~~~
@@ -282,11 +284,11 @@ References
 [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011,
 December). `Displacement interpolation using Lagrangian mass
 transport <https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf>`__.
-In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
+In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
 
 [2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of
 optimal transport <https://arxiv.org/pdf/1306.0895.pdf>`__. In Advances
-in Neural Information Processing Systems (pp. 2292-2300).
+in Neural Information Processing Systems (pp. 2292-2300).
 
 [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G.
 (2015). `Iterative Bregman projections for regularized transportation
@@ -410,14 +412,15 @@ Shift <http://proceedings.mlr.press/v89/redko19a.html>`__, Proceedings
 of the Twenty-Second International Conference on Artificial Intelligence
 and Statistics (AISTATS) 22, 2019.
 
-[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in
-optimal transport and Monge-Ampere obstacle problems]
-(http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of
-mathematics, 673-730.
+[28] Caffarelli, L. A., McCann, R. J. (2020). `Free boundaries in
+optimal transport and Monge-Ampere obstacle
+problems <http://www.math.toronto.edu/~mccann/papers/annals2010.pdf>`__,
+Annals of mathematics, 673-730.
 
-[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial
-Gromov-Wasserstein with Applications on Positive-Unlabeled Learning"]
-(https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276.
+[29] Chapel, L., Alaya, M., Gasso, G. (2019). `Partial
+Gromov-Wasserstein with Applications on Positive-Unlabeled
+Learning <https://arxiv.org/abs/2002.08276>`__, arXiv preprint
+arXiv:2002.08276.
 
 .. |PyPI version| image:: https://badge.fury.io/py/POT.svg
    :target: https://badge.fury.io/py/POT