From 5ed4a27c8054397aae51ca49ddfcc8fa01e64db7 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Mon, 20 Apr 2020 09:09:57 +0200 Subject: partial update refs --- ot/partial.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) (limited to 'ot') diff --git a/ot/partial.py b/ot/partial.py index f325d98..5f4b836 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -702,7 +702,7 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, - 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 [3]_ + The formulation of the problem has been proposed in [3]_ (prop. 5) Parameters @@ -843,7 +843,8 @@ def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - m is the amount of mass to be transported - The formulation of the problem has been proposed in [12]. + The formulation of the GW problem has been proposed in [12]_ and the + partial GW in [29]_. Parameters ---------- @@ -903,6 +904,9 @@ def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. See Also -------- @@ -979,7 +983,8 @@ def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - m is the amount of mass to be transported - The formulation of the problem has been proposed in [12]. + The formulation of the GW problem has been proposed in [12]_ and the + partial GW in [29]_. Parameters @@ -1033,6 +1038,9 @@ def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. """ partial_gw, log_gw = entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, -- cgit v1.2.3