clean and complete the document of fgw related functions

1 files changed, 20 insertions, 20 deletions

diff --git a/ot/gromov.py b/ot/gromov.py
index 7ad7e59..e329c70 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -433,8 +433,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
 

     where :

     - M is the (ns,nt) metric cost matrix

-    - :math:`f` is the regularization term ( and df is its gradient)

-    - a and b are source and target weights (sum to 1)

+    - p and q are source and target weights (sum to 1)

     - L is a loss function to account for the misfit between the similarity matrices

 

     The algorithm used for solving the problem is conditional gradient as discussed in  [24]_

@@ -453,17 +452,13 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
         Distribution in the target space

     loss_fun : str, optional

         Loss function used for the solver

-    max_iter : int, optional

-        Max number of iterations

-    tol : float, optional

-        Stop threshold on error (>0)

-    verbose : bool, optional

-        Print information along iterations

-    log : bool, optional

-        record log if True

+    alpha : float, optional

+        Trade-off parameter (0 < alpha < 1)

     armijo : bool, optional

         If True the steps of the line-search is found via an armijo research. Else closed form is used.

         If there is convergence issues use False.

+    log : bool, optional

+        record log if True

     **kwargs : dict

         parameters can be directly passed to the ot.optim.cg solver

 

@@ -515,8 +510,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
 

     where :

     - M is the (ns,nt) metric cost matrix

-    - :math:`f` is the regularization term ( and df is its gradient)

-    - a and b are source and target weights (sum to 1)

+    - p and q are source and target weights (sum to 1)

     - L is a loss function to account for the misfit between the similarity matrices

     The algorithm used for solving the problem is conditional gradient as discussed in  [1]_

 

@@ -534,17 +528,13 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
         Distribution in the target space.

     loss_fun : str, optional

         Loss function used for the solver.

-    max_iter : int, optional

-        Max number of iterations

-    tol : float, optional

-        Stop threshold on error (>0)

-    verbose : bool, optional

-        Print information along iterations

-    log : bool, optional

-        Record log if True.

+    alpha : float, optional

+        Trade-off parameter (0 < alpha < 1)

     armijo : bool, optional

         If True the steps of the line-search is found via an armijo research.

         Else closed form is used. If there is convergence issues use False.

+    log : bool, optional

+        Record log if True.

     **kwargs : dict

         Parameters can be directly pased to the ot.optim.cg solver.

 

@@ -994,6 +984,16 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
         Whether to fix the structure of the barycenter during the updates

     fixed_features : bool

         Whether to fix the feature of the barycenter during the updates

+    loss_fun : str

+        Loss function used for the solver either 'square_loss' or 'kl_loss'

+    max_iter : int, optional

+        Max number of iterations        

+    tol : float, optional

+        Stop threshol on error (>0).

+    verbose : bool, optional

+        Print information along iterations.

+    log : bool, optional

+        Record log if True.        

     init_C : ndarray, shape (N,N), optional

         Initialization for the barycenters' structure matrix. If not set

         a random init is used.