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diff --git a/docs/source/auto_examples/plot_OTDA_2D.rst b/docs/source/auto_examples/plot_OTDA_2D.rst deleted file mode 100644 index b535bb0..0000000 --- a/docs/source/auto_examples/plot_OTDA_2D.rst +++ /dev/null @@ -1,175 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OTDA_2D.py: - - -============================== -OT for empirical distributions -============================== - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_002.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_003.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_004.png - :scale: 47 - - - - - -.. code-block:: python - - - import numpy as np - import matplotlib.pylab as pl - import ot - - - - #%% parameters - - n=150 # nb bins - - xs,ys=ot.datasets.get_data_classif('3gauss',n) - xt,yt=ot.datasets.get_data_classif('3gauss2',n) - - a,b = ot.unif(n),ot.unif(n) - # loss matrix - M=ot.dist(xs,xt) - #M/=M.max() - - #%% plot samples - - pl.figure(1) - - pl.subplot(2,2,1) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.legend(loc=0) - pl.title('Source distributions') - - pl.subplot(2,2,2) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - pl.legend(loc=0) - pl.title('target distributions') - - pl.figure(2) - pl.imshow(M,interpolation='nearest') - pl.title('Cost matrix M') - - - #%% OT estimation - - # EMD - G0=ot.emd(a,b,M) - - # sinkhorn - lambd=1e-1 - Gs=ot.sinkhorn(a,b,M,lambd) - - - # Group lasso regularization - reg=1e-1 - eta=1e0 - Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) - - - #%% visu matrices - - pl.figure(3) - - pl.subplot(2,3,1) - pl.imshow(G0,interpolation='nearest') - pl.title('OT matrix ') - - pl.subplot(2,3,2) - pl.imshow(Gs,interpolation='nearest') - pl.title('OT matrix Sinkhorn') - - pl.subplot(2,3,3) - pl.imshow(Gg,interpolation='nearest') - pl.title('OT matrix Group lasso') - - pl.subplot(2,3,4) - ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - - pl.subplot(2,3,5) - ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - pl.subplot(2,3,6) - ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - #%% sample interpolation - - xst0=n*G0.dot(xt) - xsts=n*Gs.dot(xt) - xstg=n*Gg.dot(xt) - - pl.figure(4) - pl.subplot(2,3,1) - - - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) - pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples') - pl.legend(loc=0) - - pl.subplot(2,3,2) - - - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) - pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples Sinkhorn') - - pl.subplot(2,3,3) - - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) - pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples Grouplasso') -**Total running time of the script:** ( 0 minutes 17.372 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OTDA_2D.py <plot_OTDA_2D.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OTDA_2D.ipynb <plot_OTDA_2D.ipynb>` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ |