.. _sphx_glr_auto_examples_demo_OT_2D_sampleslarge.py: Demo for 2D Optimal transport between empirical distributions @author: rflamary .. code-block:: python import numpy as np import matplotlib.pylab as pl import ot #%% parameters and data generation n=5000 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) mu_t=np.array([4,4]) cov_t=np.array([[1,-.8],[-.8,1]]) xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples # loss matrix M=ot.dist(xs,xt) M/=M.max() #%% plot samples #pl.figure(1) #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') #pl.legend(loc=0) #pl.title('Source and traget distributions') # #pl.figure(2) #pl.imshow(M,interpolation='nearest') #pl.title('Cost matrix M') # #%% EMD G0=ot.emd(a,b,M) #pl.figure(3) #pl.imshow(G0,interpolation='nearest') #pl.title('OT matrix G0') # #pl.figure(4) #ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') #pl.legend(loc=0) #pl.title('OT matrix with samples') #%% sinkhorn # reg term lambd=5e-3 Gs=ot.sinkhorn(a,b,M,lambd) #pl.figure(5) #pl.imshow(Gs,interpolation='nearest') #pl.title('OT matrix sinkhorn') # #pl.figure(6) #ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') #pl.legend(loc=0) #pl.title('OT matrix Sinkhorn with samples') # **Total running time of the script:** ( 0 minutes 0.000 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: demo_OT_2D_sampleslarge.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: demo_OT_2D_sampleslarge.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_