具有分解耦合的最优传输

二维经验分布之间因子耦合OT的示例

# Author: Remi Flamary <remi.flamary@polytechnique.edu>
#
# License: MIT License

# sphinx_gallery_thumbnail_number = 2

import numpy as np
import matplotlib.pylab as pl
import ot
import ot.plot

生成数据并绘制图形

# parameters and data generation

np.random.seed(42)

n = 100  # nb samples

xs = np.random.rand(n, 2) - 0.5

xs = xs + np.sign(xs)

xt = np.random.rand(n, 2) - 0.5

a, b = ot.unif(n), ot.unif(n)  # uniform distribution on samples

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')

计算分解的 OT 和精确的 OT 解

M = ot.dist(xs, xt)
G0 = ot.emd(a, b, M)

Ga, Gb, xb = ot.factored_optimal_transport(xs, xt, a, b, r=4)

绘制因素化最优传输和精确最优传输解决方案

pl.figure(2, (14, 4))

pl.subplot(1, 3, 1)
ot.plot.plot2D_samples_mat(xs, xt, G0, c=[0.2, 0.2, 0.2], alpha=0.1)
pl.plot(xs[:, 0], xs[:, 1], "+b", label="Source samples")
pl.plot(xt[:, 0], xt[:, 1], "xr", label="Target samples")
pl.title("Exact OT with samples")

pl.subplot(1, 3, 2)
ot.plot.plot2D_samples_mat(xs, xb, Ga, c=[0.6, 0.6, 0.9], alpha=0.5)
ot.plot.plot2D_samples_mat(xb, xt, Gb, c=[0.9, 0.6, 0.6], alpha=0.5)
pl.plot(xs[:, 0], xs[:, 1], "+b", label="Source samples")
pl.plot(xt[:, 0], xt[:, 1], "xr", label="Target samples")
pl.plot(xb[:, 0], xb[:, 1], "og", label="Template samples")
pl.title("Factored OT with template samples")

pl.subplot(1, 3, 3)
ot.plot.plot2D_samples_mat(xs, xt, Ga.dot(Gb), c=[0.2, 0.2, 0.2], alpha=0.1)
pl.plot(xs[:, 0], xs[:, 1], "+b", label="Source samples")
pl.plot(xt[:, 0], xt[:, 1], "xr", label="Target samples")
pl.title("Factored OT low rank OT plan")

Text(0.5, 1.0, 'Factored OT low rank OT plan')