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:orphan:
.. To get rid of WARNING: document isn't included in any toctree
Wasserstein distance user manual
================================
Definition
----------
.. include:: wasserstein_distance_sum.inc
Functions
---------
This 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
This other implementation comes from `Hera
<https://bitbucket.org/grey_narn/hera/src/master/>`_ (BSD-3-Clause) which is
based on "Geometry Helps to Compare Persistence Diagrams"
:cite:`Kerber:2017:GHC:3047249.3064175` by Michael Kerber, Dmitriy
Morozov, and Arnur Nigmetov.
.. autofunction:: gudhi.hera.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, order=1., internal_p=2.)
print(message)
The output is:
.. testoutput::
Wasserstein distance value = 1.45
We can also have access to the optimal matching by letting `matching=True`.
It is encoded as a list of indices (i,j), meaning that the i-th point in X
is mapped to the j-th point in Y.
An index of -1 represents the diagonal.
.. 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], [5, 6], [9.5, 14.1]])
cost, matching = gudhi.wasserstein.wasserstein_distance(diag1, diag2, matching=True, order=1., internal_p=2.)
message = "Wasserstein distance value = %.2f, optimal matching: %s" %(cost, matching)
print(message)
The output is:
.. testoutput::
Wasserstein distance value = 2.15, optimal matching: [(0, 0), (1, 2), (2, -1), (-1, 1)]
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