diff options
Diffstat (limited to 'docs/source/auto_examples/plot_compute_emd.rst')
-rw-r--r-- | docs/source/auto_examples/plot_compute_emd.rst | 65 |
1 files changed, 36 insertions, 29 deletions
diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index 4c7445b..f2e2005 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -7,7 +7,6 @@ 1D optimal transport ==================== -@author: rflamary @@ -32,6 +31,10 @@ .. code-block:: python + # Author: Remi Flamary <remi.flamary@unice.fr> + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl import ot @@ -40,64 +43,68 @@ #%% parameters - n=100 # nb bins - n_target=50 # nb target distributions + n = 100 # nb bins + n_target = 50 # nb target distributions # bin positions - x=np.arange(n,dtype=np.float64) + x = np.arange(n, dtype=np.float64) - lst_m=np.linspace(20,90,n_target) + lst_m = np.linspace(20, 90, n_target) # Gaussian distributions - a=gauss(n,m=20,s=5) # m= mean, s= std + a = gauss(n, m=20, s=5) # m= mean, s= std - B=np.zeros((n,n_target)) + B = np.zeros((n, n_target)) - for i,m in enumerate(lst_m): - B[:,i]=gauss(n,m=m,s=5) + 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() + 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.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.subplot(2, 1, 2) + pl.plot(x, B, label='Target distributions') pl.title('Target distributions') + pl.tight_layout() #%% 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 + 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.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) + reg = 1e-2 + d_sinkhorn = ot.sinkhorn2(a, B, M, reg) + d_sinkhorn2 = ot.sinkhorn2(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.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() -**Total running time of the script:** ( 0 minutes 0.521 seconds) + + pl.show() + +**Total running time of the script:** ( 0 minutes 0.906 seconds) |