Optimal Transport solvers comparison

This example illustrates the solutions returns for different variants of exact, regularized and unbalanced OT solvers.

# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
# sphinx_gallery_thumbnail_number = 3

import numpy as np
import matplotlib.pylab as pl
import ot
import ot.plot
from ot.datasets import make_1D_gauss as gauss

Generate data

n = 50  # nb bins

# bin positions
x = np.arange(n, dtype=np.float64)

# Gaussian distributions
a = 0.6 * gauss(n, m=15, s=5) + 0.4 * gauss(n, m=35, s=5)  # m= mean, s= std
b = gauss(n, m=25, s=5)

# loss matrix
M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
M /= M.max()

Plot distributions and loss matrix

pl.figure(1, figsize=(6.4, 3))
pl.plot(x, a, "b", label="Source distribution")
pl.plot(x, b, "r", label="Target distribution")
pl.legend()