From 0ed4c3bba47d1375acb49596db2c863c38e9a090 Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Mon, 11 May 2020 08:39:11 +0200 Subject: Fix #299 --- src/python/doc/wasserstein_distance_user.rst | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) (limited to 'src/python/doc/wasserstein_distance_user.rst') diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index c443bab5..2d2e2ae7 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -17,12 +17,21 @@ are measured in norm p, for :math:`1 \leq p \leq \infty`. Distance Functions ------------------ -This first implementation uses the Python Optimal Transport library and is based -on ideas from "Large Scale Computation of Means and Cluster for Persistence + +Optimal Transport +***************** + +:Requires: `Python Optimal Transport `__ (POT) :math:`\geq` 0.5.1 + +This first implementation uses the `Python Optimal Transport `__ +library and is based on ideas from "Large Scale Computation of Means and Cluster for Persistence Diagrams via Optimal Transport" :cite:`10.5555/3327546.3327645`. .. autofunction:: gudhi.wasserstein.wasserstein_distance +Hera +**** + This other implementation comes from `Hera `_ (BSD-3-Clause) which is based on "Geometry Helps to Compare Persistence Diagrams" @@ -94,6 +103,8 @@ The output is: Barycenters ----------- +:Requires: `Python Optimal Transport `__ (POT) :math:`\geq` 0.5.1 + A Frechet mean (or barycenter) is a generalization of the arithmetic mean in a non linear space such as the one of persistence diagrams. Given a set of persistence diagrams :math:`\mu_1 \dots \mu_n`, it is -- cgit v1.2.3