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author | Rémi Flamary <remi.flamary@gmail.com> | 2017-09-01 15:31:44 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2017-09-01 15:31:44 +0200 |
commit | 062071b20d1d40c64bb619931bd11bd28e780485 (patch) | |
tree | 74bfcd48bb65304c2a5be74c24cdff29bd82ba4b /docs/source/auto_examples/demo_OT_2D_sampleslarge.rst | |
parent | 212f3889b1114026765cda0134e02766daa82af2 (diff) |
update example with rst titles
Diffstat (limited to 'docs/source/auto_examples/demo_OT_2D_sampleslarge.rst')
-rw-r--r-- | docs/source/auto_examples/demo_OT_2D_sampleslarge.rst | 106 |
1 files changed, 0 insertions, 106 deletions
diff --git a/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst b/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst deleted file mode 100644 index f5dbb0d..0000000 --- a/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst +++ /dev/null @@ -1,106 +0,0 @@ - - -.. _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 <demo_OT_2D_sampleslarge.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: demo_OT_2D_sampleslarge.ipynb <demo_OT_2D_sampleslarge.ipynb>` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ |