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authortlacombe <lacombe1993@gmail.com>2020-01-08 09:44:36 +0100
committertlacombe <lacombe1993@gmail.com>2020-01-08 09:44:36 +0100
commit4bcdd64974900302f420fb08435275cc8faa794a (patch)
tree4ecf27cd85ab7f680c84ef6d6f6abe0bf4c552c8
parentf3dc8e802d2a6226532f92a252f96ddbd7b6a411 (diff)
update variable name in doc
-rw-r--r--src/python/doc/wasserstein_distance_sum.inc6
-rw-r--r--src/python/doc/wasserstein_distance_user.rst2
2 files changed, 4 insertions, 4 deletions
diff --git a/src/python/doc/wasserstein_distance_sum.inc b/src/python/doc/wasserstein_distance_sum.inc
index ffd4d312..a97f428d 100644
--- a/src/python/doc/wasserstein_distance_sum.inc
+++ b/src/python/doc/wasserstein_distance_sum.inc
@@ -2,12 +2,12 @@
:widths: 30 50 20
+-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
- | .. figure:: | The p-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe |
+ | .. 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 |
| | 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`. | |
+ | 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 |
+ | 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 |
+-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
| * :doc:`wasserstein_distance_user` | |
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst
index a049cfb5..8862a5ce 100644
--- a/src/python/doc/wasserstein_distance_user.rst
+++ b/src/python/doc/wasserstein_distance_user.rst
@@ -30,7 +30,7 @@ Note that persistence diagrams must be submitted as (n x 2) numpy arrays and mus
diag1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]])
diag2 = np.array([[2.8, 4.45],[9.5, 14.1]])
- message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, q=2., p=1.)
+ message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, internal_p=2., q=1.)
print(message)
The output is: