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-rw-r--r--ot/gromov/_bregman.py6
1 files changed, 3 insertions, 3 deletions
diff --git a/ot/gromov/_bregman.py b/ot/gromov/_bregman.py
index b0cccfb..aa25f1f 100644
--- a/ot/gromov/_bregman.py
+++ b/ot/gromov/_bregman.py
@@ -69,7 +69,7 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, symmetric=None,
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).
G0: array-like, shape (ns,nt), optional
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.
@@ -152,7 +152,7 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, symmetric=None,
def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, symmetric=None, G0=None,
max_iter=1000, tol=1e-9, verbose=False, log=False):
r"""
- Returns the entropic gromov-wasserstein discrepancy between the two measured similarity matrices :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`
+ Returns the entropic Gromov-Wasserstein discrepancy between the two measured similarity matrices :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`
The function solves the following optimization problem:
@@ -194,7 +194,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, symmetric=None
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).
G0: array-like, shape (ns,nt), optional
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.