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# -*- coding: utf-8 -*-
"""
====================
1D optimal transport
====================
@author: rflamary
"""
import numpy as np
import matplotlib.pylab as pl
import ot
from ot.datasets import get_1D_gauss as gauss
#%% parameters
n=100 # nb bins
n_target=50 # nb target distributions
# bin positions
x=np.arange(n,dtype=np.float64)
lst_m=np.linspace(20,90,n_target)
# Gaussian distributions
a=gauss(n,m=20,s=5) # m= mean, s= std
B=np.zeros((n,n_target))
for i,m in enumerate(lst_m):
B[:,i]=gauss(n,m=m,s=5)
# loss matrix and normalization
M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')
M/=M.max()
M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')
M2/=M2.max()
#%% plot the distributions
pl.figure(1)
pl.subplot(2,1,1)
pl.plot(x,a,'b',label='Source distribution')
pl.title('Source distribution')
pl.subplot(2,1,2)
pl.plot(x,B,label='Target distributions')
pl.title('Target distributions')
#%% Compute and plot distributions and loss matrix
d_emd=ot.emd2(a,B,M) # direct computation of EMD
d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3
pl.figure(2)
pl.plot(d_emd,label='Euclidean EMD')
pl.plot(d_emd2,label='Squared Euclidean EMD')
pl.title('EMD distances')
pl.legend()
#%%
reg=1e-2
d_sinkhorn=ot.sinkhorn(a,B,M,reg)
d_sinkhorn2=ot.sinkhorn(a,B,M2,reg)
pl.figure(2)
pl.clf()
pl.plot(d_emd,label='Euclidean EMD')
pl.plot(d_emd2,label='Squared Euclidean EMD')
pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')
pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')
pl.title('EMD distances')
pl.legend()
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