平滑和稀疏 OT 示例

此示例说明了平滑和稀疏（KL和L2正则化）OT的计算，以及稀疏约束OT的计算及其可视化。

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

# sphinx_gallery_thumbnail_number = 5

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

生成数据

n = 100  # nb bins

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

# Gaussian distributions
a = gauss(n, m=20, s=5)  # m= mean, s= std
b = gauss(n, m=60, s=10)

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

绘制分布和损失矩阵

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()

<matplotlib.legend.Legend object at 0x7f590d7a33d0>

pl.figure(2, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, M, "Cost matrix M")

(<Axes: >, <Axes: >, <Axes: >)

解决平滑OT

lambd = 2e-3
Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type="kl")

pl.figure(3, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, Gsm, "OT matrix Smooth OT KL reg.")

pl.show()

lambd = 1e-1
Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type="l2")

pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, Gsm, "OT matrix Smooth OT l2 reg.")

pl.show()

lambd = 1e-1

max_nz = 2  # two non-zero entries are permitted per column of the OT plan
Gsc = ot.smooth.smooth_ot_dual(
    a, b, M, lambd, reg_type="sparsity_constrained", max_nz=max_nz
)
pl.figure(5, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, Gsc, "Sparsity constrained OT matrix; k=2.")

pl.show()