blob: ffd4d3122be8c1d8029a61323b4f36ac7b9827cd (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
|
.. table::
:widths: 30 50 20
+-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
| .. figure:: | The p-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 |
| | diagonal points), where the value of a matching is defined as the | |
| Wasserstein distance is the p-th root of the sum of the | p-th root of the sum of all edge lengths to the power p. Edge lengths| :Copyright: MIT |
| edge lengths to the power p. | are measured in norm q, for :math:`1 \leq q \leq \infty`. | |
| | | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 |
+-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
| * :doc:`wasserstein_distance_user` | |
+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------+
|