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| author | Marc Glisse <marc.glisse@inria.fr> | 2019-11-05 23:01:31 +0100 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2019-11-05 23:01:31 +0100 |
| commit | 94391b1cc232c5f66ae3cdadf865554c57f1308a (patch) | |
| tree | 05621622d3f41638ab062a6ec825d28308937eea /src/python/doc/wasserstein_distance_user.rst | |
| parent | 6e5f3f2c5ed908774c9005fa3ba07694bb2c6b0c (diff) | |
Create GUDHI_PYTHON_MODULES_EXTRA without auto-import
Put Wasserstein in it.
Diffstat (limited to 'src/python/doc/wasserstein_distance_user.rst')
| -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 bcb7f19d..fc7bd82c 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: |