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+
+
+.. _sphx_glr_auto_examples_demo_OT_1D_test.py:
+
+
+Demo for 1D optimal transport
+
+@author: rflamary
+
+
+
+.. code-block:: python
+
+
+ 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
+
+ # bin positions
+ x=np.arange(n,dtype=np.float64)
+
+ # Gaussian distributions
+ a=gauss(n,m=n*.2,s=5) # m= mean, s= std
+ b=gauss(n,m=n*.6,s=10)
+
+ # loss matrix
+ M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))
+ M/=M.max()
+
+ #%% plot the distributions
+
+ pl.figure(1)
+ pl.plot(x,a,'b',label='Source distribution')
+ pl.plot(x,b,'r',label='Target distribution')
+ pl.legend()
+
+ #%% plot distributions and loss matrix
+
+ pl.figure(2)
+ ot.plot.plot1D_mat(a,b,M,'Cost matrix M')
+
+ #%% EMD
+
+ G0=ot.emd(a,b,M)
+
+ pl.figure(3)
+ ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')
+
+ #%% Sinkhorn
+
+ lambd=1e-3
+ Gs=ot.sinkhorn(a,b,M,lambd,verbose=True)
+
+ pl.figure(4)
+ ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')
+
+ #%% Sinkhorn
+
+ lambd=1e-4
+ Gss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True)
+ Gss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart'])
+
+ pl.figure(5)
+ ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')
+
+ #%% Sinkhorn
+
+ lambd=1e-11
+ Gss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True)
+
+ pl.figure(5)
+ ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')
+
+**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_1D_test.py <demo_OT_1D_test.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Jupyter notebook: demo_OT_1D_test.ipynb <demo_OT_1D_test.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+ `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_