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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
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
m=6000 # nb bins
mean1 = 1000
mean2 = 1100
tol = 1e-6
# bin positions
x=np.arange(n,dtype=np.float64)
y=np.arange(m,dtype=np.float64)
# Gaussian distributions
a=gauss(n,m=mean1,s=5) # m= mean, s= std
b=gauss(m,m=mean2,s=10)
# loss matrix
M=ot.dist(x.reshape((-1,1)), y.reshape((-1,1))) ** (1./2)
print M[0,:]
#M/=M.max()
#%%
print('Computing {} EMD '.format(1))
# emd loss 1 proc
ot.tic()
G = ot.emd(a,b,M)
ot.toc('1 proc : {} s')
cost1 = (G * M).sum()
# emd loss 1 proc
ot.tic()
cost_emd2 = ot.emd2(a,b,M)
ot.toc('1 proc : {} s')
ot.tic()
G = ot.emd(b, a, np.ascontiguousarray(M.T))
ot.toc('1 proc : {} s')
cost2 = (G * M.T).sum()
assert np.abs(cost1-cost_emd2)/np.abs(cost1) < tol
assert np.abs(cost1-cost2)/np.abs(cost1) < tol
assert np.abs(cost1-np.abs(mean1-mean2))/np.abs(cost1) < tol
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