From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:48:37 +0200 Subject: [MRG] Actually run sphinx-gallery (#146) * generate gallery * remove mock * add sklearn to requirermnt?txt for example * remove latex from fgw example * add networks for graph example * remove all * add requirement.txt rtd * rtd debug * update readme * eradthedoc with redirection * add conf rtd --- docs/source/auto_examples/plot_OT_L1_vs_L2.rst | 343 ------------------------- 1 file changed, 343 deletions(-) delete mode 100644 docs/source/auto_examples/plot_OT_L1_vs_L2.rst (limited to 'docs/source/auto_examples/plot_OT_L1_vs_L2.rst') diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst deleted file mode 100644 index 16b20f9..0000000 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ /dev/null @@ -1,343 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_OT_L1_vs_L2.py: - - -========================================== -2D Optimal transport for different metrics -========================================== - -2D OT on empirical distributio with different gound metric. - -Stole the figure idea from Fig. 1 and 2 in -https://arxiv.org/pdf/1706.07650.pdf - - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - - -Dataset 1 : uniform sampling ----------------------------- - - -.. code-block:: default - - - n = 20 # nb samples - xs = np.zeros((n, 2)) - xs[:, 0] = np.arange(n) + 1 - xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... - - xt = np.zeros((n, 2)) - xt[:, 1] = np.arange(n) + 1 - - a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - - # loss matrix - M1 = ot.dist(xs, xt, metric='euclidean') - M1 /= M1.max() - - # loss matrix - M2 = ot.dist(xs, xt, metric='sqeuclidean') - M2 /= M2.max() - - # loss matrix - Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) - Mp /= Mp.max() - - # Data - pl.figure(1, figsize=(7, 3)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - pl.title('Source and target distributions') - - - # Cost matrices - pl.figure(2, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - pl.imshow(M1, interpolation='nearest') - pl.title('Euclidean cost') - - pl.subplot(1, 3, 2) - pl.imshow(M2, interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1, 3, 3) - pl.imshow(Mp, interpolation='nearest') - pl.title('Sqrt Euclidean cost') - pl.tight_layout() - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_001.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_002.png - :class: sphx-glr-multi-img - - - - - -Dataset 1 : Plot OT Matrices ----------------------------- - - -.. code-block:: default - - G1 = ot.emd(a, b, M1) - G2 = ot.emd(a, b, M2) - Gp = ot.emd(a, b, Mp) - - # OT matrices - pl.figure(3, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - ot.plot.plot2D_samples_mat(xs, xt, G1, 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.axis('equal') - # pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1, 3, 2) - ot.plot.plot2D_samples_mat(xs, xt, G2, 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.axis('equal') - # pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1, 3, 3) - ot.plot.plot2D_samples_mat(xs, xt, Gp, 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.axis('equal') - # pl.legend(loc=0) - pl.title('OT sqrt Euclidean') - pl.tight_layout() - - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:113: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Dataset 2 : Partial circle --------------------------- - - -.. code-block:: default - - - n = 50 # nb samples - xtot = np.zeros((n + 1, 2)) - xtot[:, 0] = np.cos( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - xtot[:, 1] = np.sin( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - - xs = xtot[:n, :] - xt = xtot[1:, :] - - a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - - # loss matrix - M1 = ot.dist(xs, xt, metric='euclidean') - M1 /= M1.max() - - # loss matrix - M2 = ot.dist(xs, xt, metric='sqeuclidean') - M2 /= M2.max() - - # loss matrix - Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) - Mp /= Mp.max() - - - # Data - pl.figure(4, figsize=(7, 3)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - pl.title('Source and traget distributions') - - - # Cost matrices - pl.figure(5, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - pl.imshow(M1, interpolation='nearest') - pl.title('Euclidean cost') - - pl.subplot(1, 3, 2) - pl.imshow(M2, interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1, 3, 3) - pl.imshow(Mp, interpolation='nearest') - pl.title('Sqrt Euclidean cost') - pl.tight_layout() - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_004.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png - :class: sphx-glr-multi-img - - - - - -Dataset 2 : Plot OT Matrices ------------------------------ - - -.. code-block:: default - - G1 = ot.emd(a, b, M1) - G2 = ot.emd(a, b, M2) - Gp = ot.emd(a, b, Mp) - - # OT matrices - pl.figure(6, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - ot.plot.plot2D_samples_mat(xs, xt, G1, 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.axis('equal') - # pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1, 3, 2) - ot.plot.plot2D_samples_mat(xs, xt, G2, 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.axis('equal') - # pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1, 3, 3) - ot.plot.plot2D_samples_mat(xs, xt, Gp, 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.axis('equal') - # pl.legend(loc=0) - pl.title('OT sqrt Euclidean') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_006.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:208: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.002 seconds) - - -.. _sphx_glr_download_auto_examples_plot_OT_L1_vs_L2.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_OT_L1_vs_L2.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3