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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>`_