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author | tlacombe <lacombe1993@gmail.com> | 2020-03-10 18:03:21 +0100 |
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committer | tlacombe <lacombe1993@gmail.com> | 2020-03-10 18:03:21 +0100 |
commit | fc4e10863d103ee6bc22863f48548fe246a3ddd6 (patch) | |
tree | 22290943130c055a06572160a2a7affc897c1f10 /src/python/gudhi | |
parent | 4aea5deab6ce4cbb491f4c9c2b7e9f023efbbe01 (diff) |
correction of typo in the doc
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/wasserstein.py | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/src/python/gudhi/wasserstein.py b/src/python/gudhi/wasserstein.py index 9e4dc7d5..12337780 100644 --- a/src/python/gudhi/wasserstein.py +++ b/src/python/gudhi/wasserstein.py @@ -30,10 +30,10 @@ def _build_dist_matrix(X, Y, order=2., internal_p=2.): :param order: exponent for the Wasserstein metric. :param internal_p: Ground metric (i.e. norm L^p). :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]) + 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. - note also that C[n+1, m+1] = 0 (it costs nothing to move from the diagonal to 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) Ydiag = _proj_on_diag(Y) |