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author | Vincent Rouvreau <10407034+VincentRouvreau@users.noreply.github.com> | 2019-11-07 14:37:59 +0100 |
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committer | GitHub <noreply@github.com> | 2019-11-07 14:37:59 +0100 |
commit | 753e32e5ab3c4f68c0e8a61a0a4289dcfd0cd509 (patch) | |
tree | 70017dc639f12f951d2ec5986fc2e702219745c8 /src/python/doc | |
parent | 31903a79c915b221b1d31776af49720376f9c8bd (diff) | |
parent | acd8a5a49083cd588aaa859dc4af104ce9090b22 (diff) |
Merge pull request #128 from mglisse/extra-modules
Fix #125
Diffstat (limited to 'src/python/doc')
-rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 9ec64e93..a049cfb5 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -13,7 +13,7 @@ This implementation is based on ideas from "Large Scale Computation of Means and Function -------- -.. autofunction:: gudhi.wasserstein_distance +.. autofunction:: gudhi.wasserstein.wasserstein_distance Basic example @@ -24,13 +24,13 @@ Note that persistence diagrams must be submitted as (n x 2) numpy arrays and mus .. testcode:: - import gudhi + import gudhi.wasserstein import numpy as np diag1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) diag2 = np.array([[2.8, 4.45],[9.5, 14.1]]) - message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein_distance(diag1, diag2, q=2., p=1.) + message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, q=2., p=1.) print(message) The output is: |