From dc942ac386423870277ea69fae723f216ea61030 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Fri, 17 Apr 2020 09:08:02 +0200 Subject: partial with readme updated --- ot/partial.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) (limited to 'ot/partial.py') diff --git a/ot/partial.py b/ot/partial.py index 726a590..d32e054 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -209,7 +209,7 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in optimal transport and Monge-Ampere obstacle problems. Annals of mathematics, 673-730. - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. @@ -314,7 +314,7 @@ def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in optimal transport and Monge-Ampere obstacle problems. Annals of mathematics, 673-730. - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. """ @@ -411,7 +411,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, - a and b are the sample weights - m is the amount of mass to be transported - The formulation of the problem has been proposed in [27]_ + The formulation of the problem has been proposed in [28]_ Parameters @@ -477,7 +477,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, References ---------- - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. @@ -570,7 +570,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, - a and b are the sample weights - m is the amount of mass to be transported - The formulation of the problem has been proposed in [27]_ + The formulation of the problem has been proposed in [28]_ Parameters @@ -627,7 +627,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, References ---------- - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. -- cgit v1.2.3