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
|
# -*- coding: utf-8 -*-
"""
demo of Optimal transport for domain adaptation
"""
import numpy as np
import matplotlib.pylab as pl
import ot
#%% parameters
n=100 # nb bins
# bin positions
x=np.arange(n,dtype=np.float64)
# Gaussian distributions
a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std
b=ot.datasets.get_1D_gauss(n,m=60,s=60)
# loss matrix
M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))
M/=M.max()
#%% EMD
G0=ot.emd(a,b,M)
pl.figure(3)
ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')
#%% exampel of regularization with Frobnisu norm
def f(G):
#return 0.5*np.sum(G**2)
return np.sum(G*np.log(G))
def df(G):
# return G
return np.log(G)+1
reg=1e-3
Greg=ot.optim.cg(a,b,M,reg,f,df,verbose=True)
pl.figure(4)
ot.plot.plot1D_mat(a,b,Greg,'OT matrix G0')
|
