[DOC] Corrected spelling errors (#467)

* Fix typos in docstrings and examples

* A few more fixes

* Fix ref for `center_ot_dual` function

* Another typo

* Fix titles formatting

* Explicit empty line after math blocks

* Typo: asymmetric

* Fix code cell formatting for 1D barycenters

* Empirical

* Fix indentation for references

* Fixed all WARNINGs about title formatting

* Fix empty lines after math blocks

* Fix whitespace line

* Update changelog

* Consistent Gromov-Wasserstein

* More Gromov-Wasserstein consistency

---------

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

1 files changed, 10 insertions, 7 deletions

diff --git a/ot/gromov/_gw.py b/ot/gromov/_gw.py
index bc4719d..cdfa9a3 100644
--- a/ot/gromov/_gw.py
+++ b/ot/gromov/_gw.py
@@ -26,7 +26,7 @@ from ._utils import update_square_loss, update_kl_loss
 def gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', symmetric=None, log=False, armijo=False, G0=None,
                        max_iter=1e4, tol_rel=1e-9, tol_abs=1e-9, **kwargs):
     r"""
-    Returns the gromov-wasserstein transport between :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`
+    Returns the Gromov-Wasserstein transport between :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`
 
     The function solves the following optimization problem:
 
@@ -39,6 +39,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', symmetric=None, log
              \mathbf{\gamma}^T \mathbf{1} &= \mathbf{q}
 
              \mathbf{\gamma} &\geq 0
+
     Where :
 
     - :math:`\mathbf{C_1}`: Metric cost matrix in the source space
@@ -68,7 +69,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', symmetric=None, log
     symmetric : bool, optional
         Either C1 and C2 are to be assumed symmetric or not.
         If let to its default None value, a symmetry test will be conducted.
-        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric).
+        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
     verbose : bool, optional
         Print information along iterations
     log : bool, optional
@@ -170,7 +171,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', symmetric=None, log
 def gromov_wasserstein2(C1, C2, p, q, loss_fun='square_loss', symmetric=None, log=False, armijo=False, G0=None,
                         max_iter=1e4, tol_rel=1e-9, tol_abs=1e-9, **kwargs):
     r"""
-    Returns the gromov-wasserstein discrepancy between :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`
+    Returns the Gromov-Wasserstein discrepancy between :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`
 
     The function solves the following optimization problem:
 
@@ -183,6 +184,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun='square_loss', symmetric=None, lo
              \mathbf{\gamma}^T \mathbf{1} &= \mathbf{q}
 
              \mathbf{\gamma} &\geq 0
+
     Where :
 
     - :math:`\mathbf{C_1}`: Metric cost matrix in the source space
@@ -216,7 +218,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun='square_loss', symmetric=None, lo
     symmetric : bool, optional
         Either C1 and C2 are to be assumed symmetric or not.
         If let to its default None value, a symmetry test will be conducted.
-        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric).
+        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
     verbose : bool, optional
         Print information along iterations
     log : bool, optional
@@ -241,7 +243,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun='square_loss', symmetric=None, lo
     gw_dist : float
         Gromov-Wasserstein distance
     log : dict
-        convergence information and Coupling marix
+        convergence information and Coupling matrix
 
     References
     ----------
@@ -310,6 +312,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', symmetric=
         which can lead to copy overhead on GPU arrays.
     .. note:: All computations in the conjugate gradient solver are done with
         numpy to limit memory overhead.
+
     The algorithm used for solving the problem is conditional gradient as discussed in :ref:`[24] <references-fused-gromov-wasserstein>`
 
     Parameters
@@ -329,7 +332,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', symmetric=
     symmetric : bool, optional
         Either C1 and C2 are to be assumed symmetric or not.
         If let to its default None value, a symmetry test will be conducted.
-        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric).
+        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
     alpha : float, optional
         Trade-off parameter (0 < alpha < 1)
     armijo : bool, optional
@@ -503,7 +506,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', symmetric
     Returns
     -------
     fgw-distance : float
-        Fused gromov wasserstein distance for the given parameters.
+        Fused Gromov-Wasserstein distance for the given parameters.
     log : dict
         Log dictionary return only if log==True in parameters.