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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 10 09:56:06 2017
@author: rflamary
"""
import numpy as np
import pylab as pl
import ot
from ot.datasets import get_1D_gauss as gauss
reload(ot.lp)
#%% parameters
n=5000 # nb bins
# bin positions
x=np.arange(n,dtype=np.float64)
# Gaussian distributions
a=gauss(n,m=20,s=5) # m= mean, s= std
ls= range(20,1000,10)
nb=len(ls)
b=np.zeros((n,nb))
for i in range(nb):
b[:,i]=gauss(n,m=ls[i],s=10)
# loss matrix
M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))
#M/=M.max()
#%%
print('Computing {} EMD '.format(nb))
# emd loss 1 proc
ot.tic()
emd_loss4=ot.emd2(a,b,M,1)
ot.toc('1 proc : {} s')
# emd loss multipro proc
ot.tic()
emd_loss4=ot.emd2(a,b,M)
ot.toc('multi proc : {} s')
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