From b663b20ec315584bdf8442acc1b65f3a948c5fbc Mon Sep 17 00:00:00 2001
From: Simon Forbat <54510453+simon-forb@users.noreply.github.com>
Date: Fri, 21 Jun 2024 14:22:30 +0200
Subject: [PATCH] Update _gw.py

doc typos
---
 ot/gromov/_gw.py | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/ot/gromov/_gw.py b/ot/gromov/_gw.py
index 9dbc6b19e..86ff566ea 100644
--- a/ot/gromov/_gw.py
+++ b/ot/gromov/_gw.py
@@ -43,11 +43,11 @@ def gromov_wasserstein(C1, C2, p=None, q=None, loss_fun='square_loss', symmetric
 
     Where :
 
-    - :math:`\mathbf{C_1}`: Metric cost matrix in the source space
-    - :math:`\mathbf{C_2}`: Metric cost matrix in the target space
-    - :math:`\mathbf{p}`: distribution in the source space
-    - :math:`\mathbf{q}`: distribution in the target space
-    - `L`: loss function to account for the misfit between the similarity matrices
+    - :math:`\mathbf{C_1}`: Metric cost matrix in the source space.
+    - :math:`\mathbf{C_2}`: Metric cost matrix in the target space.
+    - :math:`\mathbf{p}`: Distribution in the source space.
+    - :math:`\mathbf{q}`: Distribution in the target space.
+    - `L`: Loss function to account for the misfit between the similarity matrices.
 
     .. note:: This function is backend-compatible and will work on arrays
         from all compatible backends. But the algorithm uses the C++ CPU backend
@@ -62,9 +62,9 @@ def gromov_wasserstein(C1, C2, p=None, q=None, loss_fun='square_loss', symmetric
     Parameters
     ----------
     C1 : array-like, shape (ns, ns)
-        Metric cost matrix in the source space
+        Metric cost matrix in the source space.
     C2 : array-like, shape (nt, nt)
-        Metric cost matrix in the target space
+        Metric cost matrix in the target space.
     p : array-like, shape (ns,), optional
         Distribution in the source space.
         If let to its default value None, uniform distribution is taken.
@@ -72,29 +72,29 @@ def gromov_wasserstein(C1, C2, p=None, q=None, loss_fun='square_loss', symmetric
         Distribution in the target space.
         If let to its default value None, uniform distribution is taken.
     loss_fun : str, optional
-        loss function used for the solver either 'square_loss' or 'kl_loss'
+        Loss function used for the solver either 'square_loss' or 'kl_loss'.
     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. asymmetric).
     verbose : bool, optional
-        Print information along iterations
+        Print information along iterations.
     log : bool, optional
-        record log if True
+        Record log if True.
     armijo : bool, optional
-        If True the step of the line-search is found via an armijo research. Else closed form is used.
-        If there are convergence issues use False.
+        If True, the step of the line-search is found via an armijo search. Else closed form is used.
+        If there are convergence issues, use False.
     G0: array-like, shape (ns,nt), optional
-        If None the initial transport plan of the solver is pq^T.
+        If None, the initial transport plan of the solver is pq^T.
         Otherwise G0 must satisfy marginal constraints and will be used as initial transport of the solver.
     max_iter : int, optional
-        Max number of iterations
+        Max number of iterations.
     tol_rel : float, optional
-        Stop threshold on relative error (>0)
+        Stop threshold on relative error (>0).
     tol_abs : float, optional
-        Stop threshold on absolute error (>0)
+        Stop threshold on absolute error (>0).
     **kwargs : dict
-        parameters can be directly passed to the ot.optim.cg solver
+        Parameters can be directly passed to the ot.optim.cg solver.
 
     Returns
     -------
@@ -175,7 +175,7 @@ def line_search(cost, G, deltaG, Mi, cost_G, **kwargs):
 
     if not nx.is_floating_point(C10):
         warnings.warn(
-            "Input structure matrix consists of integer. The transport plan will be "
+            "Input structure matrix consists of integers. The transport plan will be "
             "casted accordingly, possibly resulting in a loss of precision. "
             "If this behaviour is unwanted, please make sure your input "
             "structure matrix consists of floating point elements.",