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authorROUVREAU Vincent <vincent.rouvreau@inria.fr>2020-03-23 18:11:15 +0100
committerROUVREAU Vincent <vincent.rouvreau@inria.fr>2020-03-23 18:11:15 +0100
commitcf29f4a485d06469d17c6d12d306901fa3c5ab36 (patch)
treeb7e4595967f88e116f49c4e8a15a8d798bd8432c /src/python/doc/wasserstein_distance_sum.inc
parente1c8edc4b148331083f53c7c3d34766190bb6d99 (diff)
Shorter headers in sphinx: Introduced in -> Since and Copyright -> License
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+-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
| .. figure:: | The q-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe |
| ../../doc/Bottleneck_distance/perturb_pd.png | persistence diagrams. It's the minimum value c that can be achieved | |
- | :figclass: align-center | by a perfect matching between the points of the two diagrams (+ all | :Introduced in: GUDHI 3.1.0 |
+ | :figclass: align-center | by a perfect matching between the points of the two diagrams (+ all | :Since: GUDHI 3.1.0 |
| | diagonal points), where the value of a matching is defined as the | |
- | Wasserstein distance is the q-th root of the sum of the | q-th root of the sum of all edge lengths to the power q. Edge lengths| :Copyright: MIT |
+ | Wasserstein distance is the q-th root of the sum of the | q-th root of the sum of all edge lengths to the power q. Edge lengths| :License: MIT |
| edge lengths to the power q. | are measured in norm p, for :math:`1 \leq p \leq \infty`. | |
| | | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 |
+-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+