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 otGenerate 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 samplesPlot 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")
Text(0.5, 1.0, 'Source and target distributions')Sliced Wasserstein distance for different seeds and number of projections Plot Sliced Wasserstein Distance
Total running time of the script: (0 minutes 2.014 seconds)
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