Note

Go to the end to download the full example code.

Note

Example added in release: 0.8.0.

This example illustrates the computation of the sliced Wasserstein Distance as proposed in [31].

[31] Bonneel, Nicolas, et al. “Sliced and radon wasserstein barycenters of measures.” Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45

# Author: Adrien Corenflos <adrien.corenflos@aalto.fi>
#
# License: MIT License

# sphinx_gallery_thumbnail_number = 2

import matplotlib.pylab as pl
import numpy as np

import ot

Generate data

n = 200  # nb samples

mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])

mu_t = np.array([4, 4])
cov_t = np.array([[1, -0.8], [-0.8, 1]])

xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)

a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples

Plot data

pl.figure(1)
pl.plot(xs[:, 0], xs[:, 1], "+b", label="Source samples")
pl.plot(xt[:, 0], xt[:, 1], "xr", label="Target samples")
pl.legend(loc=0)
pl.title("Source and target distributions")
Source and target distributions
Text(0.5, 1.0, 'Source and target distributions')

Sliced Wasserstein distance for different seeds and number of projections

Plot Sliced Wasserstein Distance

Sliced Wasserstein Distance with 95% confidence interval

Total running time of the script: (0 minutes 2.410 seconds)

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