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_2D_samples.rst | 310 ----------------------- 1 file changed, 310 deletions(-) delete mode 100644 docs/source/auto_examples/plot_OT_2D_samples.rst (limited to 'docs/source/auto_examples/plot_OT_2D_samples.rst') 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 460bb95..0000000 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ /dev/null @@ -1,310 +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_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:: default - - - # Author: Remi Flamary - # Kilian Fatras - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - 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:: default - - - 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 - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - Text(0.5, 1.0, 'Cost matrix M') - - - -Compute EMD ------------ - - -.. code-block:: default - - - 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_003.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - Text(0.5, 1.0, 'OT matrix with samples') - - - -Compute Sinkhorn ----------------- - - -.. code-block:: default - - - # 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_005.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:103: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Emprirical Sinkhorn ----------------- - - -.. code-block:: default - - - # 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_007.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_008.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/ot/bregman.py:363: RuntimeWarning: divide by zero encountered in true_divide - v = np.divide(b, KtransposeU) - Warning: numerical errors at iteration 0 - /home/rflamary/PYTHON/POT/ot/plot.py:90: RuntimeWarning: invalid value encountered in double_scalars - if G[i, j] / mx > thr: - /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:128: 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 2.154 seconds) - - -.. _sphx_glr_download_auto_examples_plot_OT_2D_samples.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_2D_samples.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3