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authorThéo Lacombe <lacombe1993@gmail.com>2020-01-13 17:39:02 +0100
committerGitHub <noreply@github.com>2020-01-13 17:39:02 +0100
commit9bbbc84ab7208ccd69934445202538e34439b304 (patch)
tree887c2040ef396086b383fec962b52a2d0591afbe
parentcabafa3852c2d325b83593d34f16dfbc2d9eaefb (diff)
Update src/python/gudhi/wasserstein.py
Co-Authored-By: Marc Glisse <marc.glisse@inria.fr>
-rw-r--r--src/python/gudhi/wasserstein.py3
1 files changed, 1 insertions, 2 deletions
diff --git a/src/python/gudhi/wasserstein.py b/src/python/gudhi/wasserstein.py
index b6495c80..3eb7faef 100644
--- a/src/python/gudhi/wasserstein.py
+++ b/src/python/gudhi/wasserstein.py
@@ -27,7 +27,7 @@ def _build_dist_matrix(X, Y, order=2., internal_p=2.):
'''
:param X: (n x 2) numpy.array encoding the (points of the) first diagram.
:param Y: (m x 2) numpy.array encoding the second diagram.
- :param internal_p: Ground metric (i.e. norm l_q).
+ :param internal_p: Ground metric (i.e. norm l_p).
:param order: exponent for the Wasserstein metric.
:returns: (n+1) x (m+1) np.array encoding the cost matrix C.
For 1 <= i <= n, 1 <= j <= m, C[i,j] encodes the distance between X[i] and Y[j], while C[i, m+1] (resp. C[n+1, j]) encodes the distance (to the p) between X[i] (resp Y[j]) and its orthogonal proj onto the diagonal.
@@ -95,4 +95,3 @@ def wasserstein_distance(X, Y, order=2., internal_p=2.):
ot_cost = (n+m) * ot.emd2(a, b, M, numItermax=2000000)
return ot_cost ** (1./order)
-