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| author | Rémi Flamary <remi.flamary@gmail.com> | 2018-05-09 13:26:33 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-05-09 13:26:33 +0200 |
| commit | 27032b6fa0f2f68af3fe4f90e5dcbb68f130a962 (patch) | |
| tree | bef4b8a49cf410652e9c100274d010519acaf0f8 /ot/gromov.py | |
| parent | 1ff35860db2d612748270299d7ce0037b8d40702 (diff) | |
| parent | 0496e2b1b2c2f4ea2d7f313ccf58c612efaa70bf (diff) | |
Merge pull request #42 from rflamary/linear_mapping
Linear mapping + tests
Diffstat (limited to 'ot/gromov.py')
| -rw-r--r-- | ot/gromov.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/ot/gromov.py b/ot/gromov.py index 2a23873..65b2e29 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -595,7 +595,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, gw, logv = entropic_gromov_wasserstein(
C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True)
- log['T'] = gw
+ logv['T'] = gw
if log:
return logv['gw_dist'], logv
@@ -613,7 +613,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, The function solves the following optimization problem:
.. math::
- C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps)
+ C = argmin_C\in R^{NxN} \sum_s \lambda_s GW(C,Cs,p,ps)
Where :
|