[DOC] Fix references to JAX and numpy functions (#475)

* Fix ref to JAX

* Fix references to numpy.random.*

* Typo in CONTRIBUTING

* Removed :any: reference from func parameters

* Make markup of params consistent with other docstrings

* Mentioned latest open PR in RELEASES

* Fix See Also references for ot.factored

---------

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

Author: kachayev

.github/CONTRIBUTING.md

@@ -199,4 +199,4 @@ method does to the data and a figure (coming from an example)
 illustrating it.
 
 
-This Contribution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.
+This Contribution guide is strongly inspired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.

RELEASES.md

@@ -12,7 +12,7 @@
 
 - Fix circleci-redirector action and codecov (PR #460)
 - Fix issues with cuda for ot.binary_search_circle and with gradients for ot.sliced_wasserstein_sphere (PR #457)
-- Major documentation cleanup (PR #462, #467)
+- Major documentation cleanup (PR #462, #467, #475)
 - Fix gradients for "Wasserstein2 Minibatch GAN" example (PR #466)
 - Faster Bures-Wasserstein distance with NumPy backend (PR #468)
 - Fix issue backend for ot.sliced_wasserstein_sphere ot.sliced_wasserstein_sphere_unif (PR #471)

docs/source/conf.py

@@ -348,10 +348,11 @@ def __getattr__(cls, name):
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {'python': ('https://docs.python.org/3', None),
-                       'numpy': ('http://docs.scipy.org/doc/numpy/', None),
+                       'numpy': ('https://numpy.org/doc/stable/', None),
                        'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
                        'matplotlib': ('http://matplotlib.org/', None),
-                       'torch': ('https://pytorch.org/docs/stable/', None)}
+                       'torch': ('https://pytorch.org/docs/stable/', None),
+                       'jax': ('https://jax.readthedocs.io/en/latest/', None)}
 
 sphinx_gallery_conf = {
     'examples_dirs': ['../../examples', '../../examples/da'],

ot/backend.py

@@ -680,7 +680,7 @@ def seed(self, seed=None):
 
         This function follows the api from :any:`numpy.random.seed`
 
-        See: https://numpy.org/doc/stable/reference/generated/numpy.random.seed.html
+        See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html
         """
         raise NotImplementedError()
 
@@ -690,7 +690,7 @@ def rand(self, *size, type_as=None):
 
         This function follows the api from :any:`numpy.random.rand`
 
-        See: https://numpy.org/doc/stable/reference/generated/numpy.random.rand.html
+        See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.rand.html
         """
         raise NotImplementedError()
 
@@ -700,7 +700,7 @@ def randn(self, *size, type_as=None):
 
         This function follows the api from :any:`numpy.random.rand`
 
-        See: https://numpy.org/doc/stable/reference/generated/numpy.random.rand.html
+        See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.rand.html
         """
         raise NotImplementedError()
 

ot/factored.py

@@ -81,8 +81,8 @@ def factored_optimal_transport(Xa, Xb, a=None, b=None, reg=0.0, r=100, X0=None,
 
     See Also
     --------
-    ot.bregman.sinkhorn : Entropic regularized OT ot.optim.cg : General
-    regularized OT
+    ot.bregman.sinkhorn : Entropic regularized OT
+    ot.optim.cg : General regularized OT
     """
 
     nx = get_backend(Xa, Xb)

ot/solvers.py

@@ -28,10 +28,10 @@ def solve(M, a=None, b=None, reg=None, reg_type="KL", unbalanced=None,
         \lambda_u U(\mathbf{T}\mathbf{1},\mathbf{a}) +
         \lambda_u U(\mathbf{T}^T\mathbf{1},\mathbf{b})
 
-    The regularization is selected with :any:`reg` (:math:`\lambda_r`) and :any:`reg_type`. By
+    The regularization is selected with `reg` (:math:`\lambda_r`) and `reg_type`. By
     default ``reg=None`` and there is no regularization. The unbalanced marginal
-    penalization can be selected with :any:`unbalanced` (:math:`\lambda_u`) and
-    :any:`unbalanced_type`. By default ``unbalanced=None`` and the function
+    penalization can be selected with `unbalanced` (:math:`\lambda_u`) and
+    `unbalanced_type`. By default ``unbalanced=None`` and the function
     solves the exact optimal transport problem (respecting the marginals).
 
     Parameters
@@ -46,12 +46,12 @@ def solve(M, a=None, b=None, reg=None, reg_type="KL", unbalanced=None,
         Regularization weight :math:`\lambda_r`, by default None (no reg., exact
         OT)
     reg_type : str, optional
-        Type of regularization :math:`R`  either "KL", "L2", 'entropy', by default "KL"
+        Type of regularization :math:`R`  either "KL", "L2", "entropy", by default "KL"
     unbalanced : float, optional
         Unbalanced penalization weight :math:`\lambda_u`, by default None
         (balanced OT)
     unbalanced_type : str, optional
-        Type of unbalanced penalization unction :math:`U`  either "KL", "L2", 'TV', by default 'KL'
+        Type of unbalanced penalization unction :math:`U`  either "KL", "L2", "TV", by default "KL"
     n_threads : int, optional
         Number of OMP threads for exact OT solver, by default 1
     max_iter : int, optional