diff options
| -rw-r--r-- | src/python/gudhi/hera/wasserstein.cc | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/src/python/gudhi/hera/wasserstein.cc b/src/python/gudhi/hera/wasserstein.cc index 77c1413a..41e84f7b 100644 --- a/src/python/gudhi/hera/wasserstein.cc +++ b/src/python/gudhi/hera/wasserstein.cc @@ -147,9 +147,9 @@ PYBIND11_MODULE(wasserstein, m) { order (float): Wasserstein exponent W_q internal_p (float): Internal Minkowski norm L^p in R^2 delta (float): Relative error 1+delta - matching (bool): if ``True``, computes and returns the optimal matching between X and Y, encoded as a (n x 2) np.array [...[i,j]...], meaning the i-th point in X is matched to the j-th point in Y, with the convention that (-1) represents the diagonal. + matching (bool): if ``True``, computes and returns the optimal matching between X and Y, encoded as a (n x 2) np.array [...[i,j]...], meaning the i-th point in X is matched to the j-th point in Y, with the convention that (-1) represents the diagonal. If the distance between two diagrams is +inf (which happens if the cardinalities of essential parts differ) and the matching is requested, it will be set to ``None`` (any matching is optimal). Returns: - float|Tuple[float,numpy.array]: Approximate Wasserstein distance W_q(X,Y), and optionally the corresponding matching + float|Tuple[float,numpy.array|None]: Approximate Wasserstein distance W_q(X,Y), and optionally the corresponding matching )pbdoc"); } |