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| author | tlacombe <lacombe1993@gmail.com> | 2021-04-20 19:06:56 +0200 |
|---|---|---|
| committer | tlacombe <lacombe1993@gmail.com> | 2021-04-20 19:06:56 +0200 |
| commit | 604b2cde0c7951c81d1c510f3038e2c65c19e6fe (patch) | |
| tree | d2f22392f94fcb3c449453c79f773c2e56892ed0 /src/python/doc/wasserstein_distance_user.rst | |
| parent | bb0792ed7bfe9d718be3e8039e8fb89af6d160e5 (diff) | |
update doc and tests
Diffstat (limited to 'src/python/doc/wasserstein_distance_user.rst')
| -rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 1 |
1 files changed, 1 insertions, 0 deletions
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 091c9fd9..76eb1469 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -92,6 +92,7 @@ any matching has a cost +inf and thus can be considered to be optimal. In such a for j in dgm2_to_diagonal: print("point %s in dgm2 is matched to the diagonal" %j) + # An example where essential part cardinalities differ dgm3 = np.array([[1, 2], [0, np.inf]]) dgm4 = np.array([[1, 2], [0, np.inf], [1, np.inf]]) cost, matchings = gudhi.wasserstein.wasserstein_distance(dgm3, dgm4, matching=True, order=1, internal_p=2) |