Tests + contributions

1 files changed, 8 insertions, 4 deletions

diff --git a/ot/gromov.py b/ot/gromov.py
index ad68a1c..297b194 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -926,6 +926,10 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
         "Optimal Transport for structured data with application on graphs"

         International Conference on Machine Learning (ICML). 2019.

     """

+    

+    class UndefinedParameter(Exception):

+        pass

+    

     S = len(Cs)

     d = Ys[0].shape[1] #dimension on the node features

     if p is None:

@@ -938,7 +942,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
 

     if fixed_structure:

         if init_C is None:

-            C=Cs[0]

+            raise UndefinedParameter('If C is fixed it must be initialized')

         else:

             C=init_C

     else:

@@ -950,7 +954,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
 

     if fixed_features:

         if init_X is None:

-            X=Ys[0]

+           raise UndefinedParameter('If X is fixed it must be initialized')

         else :

             X= init_X

     else:

@@ -1004,13 +1008,13 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
         # Cs is ns,ns

         # p is N,1

         # ps is ns,1

-

+        

         T = [fused_gromov_wasserstein((1-alpha)*Ms[s],C,Cs[s],p,ps[s],loss_fun,alpha,numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)]

 

             # T is N,ns

 

         log_['Ts_iter'].append(T)

-        err_feature = np.linalg.norm(X - Xprev.reshape(d,N))

+        err_feature = np.linalg.norm(X - Xprev.reshape(N,d))

         err_structure = np.linalg.norm(C - Cprev)

 

         if log: