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diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst deleted file mode 100644 index 1f1d713..0000000 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ /dev/null @@ -1,273 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OT_2D_samples.py: - - -==================================================== -2D Optimal transport between empirical distributions -==================================================== - -Illustration of 2D optimal transport between discributions that are weighted -sum of diracs. The OT matrix is plotted with the samples. - - - - -.. code-block:: python - - - # Author: Remi Flamary <remi.flamary@unice.fr> - # Kilian Fatras <kilian.fatras@irisa.fr> - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - -Generate data -------------- - - - -.. code-block:: python - - - #%% parameters and data generation - - n = 50 # 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.make_2D_samples_gauss(n, mu_s, cov_s) - xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) - - a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples - - # loss matrix - M = ot.dist(xs, xt) - M /= M.max() - - - - - - - -Plot data ---------- - - - -.. code-block:: python - - - #%% 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 target distributions') - - pl.figure(2) - pl.imshow(M, interpolation='nearest') - pl.title('Cost matrix M') - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png - :scale: 47 - - - - -Compute EMD ------------ - - - -.. code-block:: python - - - #%% 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') - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png - :scale: 47 - - - - -Compute Sinkhorn ----------------- - - - -.. code-block:: python - - - #%% sinkhorn - - # reg term - lambd = 1e-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') - - pl.show() - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_009.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_010.png - :scale: 47 - - - - -Emprirical Sinkhorn ----------------- - - - -.. code-block:: python - - - #%% sinkhorn - - # reg term - lambd = 1e-3 - - Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) - - pl.figure(7) - pl.imshow(Ges, interpolation='nearest') - pl.title('OT matrix empirical sinkhorn') - - pl.figure(8) - ot.plot.plot2D_samples_mat(xs, xt, Ges, 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 from samples') - - pl.show() - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_013.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_014.png - :scale: 47 - - -.. rst-class:: sphx-glr-script-out - - Out:: - - Warning: numerical errors at iteration 0 - - -**Total running time of the script:** ( 0 minutes 2.616 seconds) - - - -.. only :: html - - .. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OT_2D_samples.py <plot_OT_2D_samples.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb <plot_OT_2D_samples.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |