1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
|
# -*- coding: utf-8 -*-
"""
====================================================
Spherical Sliced Wasserstein on distributions in S^2
====================================================
This example illustrates the computation of the spherical sliced Wasserstein discrepancy as
proposed in [46].
[46] Bonet, C., Berg, P., Courty, N., Septier, F., Drumetz, L., & Pham, M. T. (2023). 'Spherical Sliced-Wasserstein". International Conference on Learning Representations.
"""
# Author: Clément Bonet <clement.bonet@univ-ubs.fr>
#
# License: MIT License
# sphinx_gallery_thumbnail_number = 2
import matplotlib.pylab as pl
import numpy as np
import ot
##############################################################################
# Generate data
# -------------
# %% parameters and data generation
n = 500 # nb samples
xs = np.random.randn(n, 3)
xt = np.random.randn(n, 3)
xs = xs / np.sqrt(np.sum(xs**2, -1, keepdims=True))
xt = xt / np.sqrt(np.sum(xt**2, -1, keepdims=True))
a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples
##############################################################################
# Plot data
# ---------
# %% plot samples
fig = pl.figure(figsize=(10, 10))
ax = pl.axes(projection='3d')
ax.grid(False)
u, v = np.mgrid[0:2 * np.pi:30j, 0:np.pi:30j]
x = np.cos(u) * np.sin(v)
y = np.sin(u) * np.sin(v)
z = np.cos(v)
ax.plot_surface(x, y, z, color="gray", alpha=0.03)
ax.plot_wireframe(x, y, z, linewidth=1, alpha=0.25, color="gray")
ax.scatter(xs[:, 0], xs[:, 1], xs[:, 2], label="Source")
ax.scatter(xt[:, 0], xt[:, 1], xt[:, 2], label="Target")
fs = 10
# Labels
ax.set_xlabel('x', fontsize=fs)
ax.set_ylabel('y', fontsize=fs)
ax.set_zlabel('z', fontsize=fs)
ax.view_init(20, 120)
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_zlim(-1.5, 1.5)
# Ticks
ax.set_xticks([-1, 0, 1])
ax.set_yticks([-1, 0, 1])
ax.set_zticks([-1, 0, 1])
pl.legend(loc=0)
pl.title("Source and Target distribution")
###############################################################################
# Spherical Sliced Wasserstein for different seeds and number of projections
# --------------------------------------------------------------------------
n_seed = 50
n_projections_arr = np.logspace(0, 3, 25, dtype=int)
res = np.empty((n_seed, 25))
# %% Compute statistics
for seed in range(n_seed):
for i, n_projections in enumerate(n_projections_arr):
res[seed, i] = ot.sliced_wasserstein_sphere(xs, xt, a, b, n_projections, seed=seed, p=1)
res_mean = np.mean(res, axis=0)
res_std = np.std(res, axis=0)
###############################################################################
# Plot Spherical Sliced Wasserstein
# ---------------------------------
pl.figure(2)
pl.plot(n_projections_arr, res_mean, label=r"$SSW_1$")
pl.fill_between(n_projections_arr, res_mean - 2 * res_std, res_mean + 2 * res_std, alpha=0.5)
pl.legend()
pl.xscale('log')
pl.xlabel("Number of projections")
pl.ylabel("Distance")
pl.title('Spherical Sliced Wasserstein Distance with 95% confidence inverval')
pl.show()
|
