diff options
author | Marc Glisse <marc.glisse@inria.fr> | 2019-11-08 21:05:19 +0100 |
---|---|---|
committer | Marc Glisse <marc.glisse@inria.fr> | 2019-11-08 21:05:19 +0100 |
commit | 60c52012578265e6b6ac2e4a616cf2b617809d2c (patch) | |
tree | e958905af656f72228f9e778464739093635d35b /src/python/doc/wasserstein_distance_user.rst | |
parent | 7c80dd28eb16e70316e6acc0bde8f698f79b2003 (diff) | |
parent | db405e686cc859e510b894dca45562158cb5c963 (diff) |
Merge remote-tracking branch 'origin/master' into sklearn_tda
Diffstat (limited to 'src/python/doc/wasserstein_distance_user.rst')
-rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 40 |
1 files changed, 40 insertions, 0 deletions
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst new file mode 100644 index 00000000..a049cfb5 --- /dev/null +++ b/src/python/doc/wasserstein_distance_user.rst @@ -0,0 +1,40 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +Wasserstein distance user manual +================================ +Definition +---------- + +.. include:: wasserstein_distance_sum.inc + +This implementation is based on ideas from "Large Scale Computation of Means and Cluster for Persistence Diagrams via Optimal Transport". + +Function +-------- +.. autofunction:: gudhi.wasserstein.wasserstein_distance + + +Basic example +------------- + +This example computes the 1-Wasserstein distance from 2 persistence diagrams with euclidean ground metric. +Note that persistence diagrams must be submitted as (n x 2) numpy arrays and must not contain inf values. + +.. testcode:: + + import gudhi.wasserstein + import numpy as np + + 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.) + print(message) + +The output is: + +.. testoutput:: + + Wasserstein distance value = 1.45 |