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-rw-r--r--src/python/doc/barycenter_sum.inc8
1 files changed, 4 insertions, 4 deletions
diff --git a/src/python/doc/barycenter_sum.inc b/src/python/doc/barycenter_sum.inc
index afac07d7..da2bdd84 100644
--- a/src/python/doc/barycenter_sum.inc
+++ b/src/python/doc/barycenter_sum.inc
@@ -7,11 +7,11 @@
| :figclass: align-center | Given a set of persistence diagrams :math:`\mu_1 \dots \mu_n`, it is | :Introduced in: GUDHI 3.1.0 |
| | defined as a minimizer of the variance functional, that is of | |
| Illustration of Frechet mean between persistence | :math:`\mu \mapsto \sum_{i=1}^n d_2(\mu,\mu_i)^2`. | :Copyright: MIT |
- | diagrams. | where :math:`d_2` denotes the Wasserstein-2 distance between persis- | |
- | | tence diagrams. | |
+ | diagrams. | where :math:`d_2` denotes the Wasserstein-2 distance between | |
+ | | persistence diagrams. | |
| | It is known to exist and is generically unique. However, an exact | |
- | | computation is in general untractable. Current implementation avai- | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 |
- | | -lable is based on [Turner et al, 2014], and uses an EM-scheme to | |
+ | | computation is in general untractable. Current implementation | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 |
+ | | available is based on [Turner et al, 2014], and uses an EM-scheme to | |
| | provide a local minimum of the variance functional (somewhat similar | |
| | to the Lloyd algorithm to estimate a solution to the k-means | |
| | problem). The local minimum returned depends on the initialization of| |