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| author | Théo Lacombe <lacombe1993@gmail.com> | 2020-03-10 18:25:10 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2020-03-10 18:25:10 +0100 |
| commit | 753290475ab6e95c2de1baad97ee6f755a0ce19a (patch) | |
| tree | 62065228f69d89ceb046db2004b994680934621e /src | |
| parent | 6c369a6aa566dfcb8cdb501d0c39eafb32219669 (diff) | |
Update src/python/gudhi/wasserstein.py
Co-Authored-By: Marc Glisse <marc.glisse@inria.fr>
Diffstat (limited to 'src')
| -rw-r--r-- | src/python/gudhi/wasserstein.py | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/src/python/gudhi/wasserstein.py b/src/python/gudhi/wasserstein.py index 12337780..83a682df 100644 --- a/src/python/gudhi/wasserstein.py +++ b/src/python/gudhi/wasserstein.py @@ -32,7 +32,7 @@ def _build_dist_matrix(X, Y, order=2., internal_p=2.): :returns: (n+1) x (m+1) np.array encoding the cost matrix C. For 0 <= i < n, 0 <= j < m, C[i,j] encodes the distance between X[i] and Y[j], while C[i, m] (resp. C[n, j]) encodes the distance (to the p) between X[i] (resp Y[j]) - and its orthogonal proj onto the diagonal. + and its orthogonal projection onto the diagonal. note also that C[n, m] = 0 (it costs nothing to move from the diagonal to the diagonal). ''' Xdiag = _proj_on_diag(X) |