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_gromov.rst | 245 ------------------------------ 1 file changed, 245 deletions(-) delete mode 100644 docs/source/auto_examples/plot_gromov.rst (limited to 'docs/source/auto_examples/plot_gromov.rst') diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst deleted file mode 100644 index 13d0d09..0000000 --- a/docs/source/auto_examples/plot_gromov.rst +++ /dev/null @@ -1,245 +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_gromov.py: - - -========================== -Gromov-Wasserstein example -========================== - -This example is designed to show how to use the Gromov-Wassertsein distance -computation in POT. - - -.. code-block:: default - - - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - import scipy as sp - import numpy as np - import matplotlib.pylab as pl - from mpl_toolkits.mplot3d import Axes3D # noqa - import ot - - - - - - - - -Sample two Gaussian distributions (2D and 3D) ---------------------------------------------- - -The Gromov-Wasserstein distance allows to compute distances with samples that -do not belong to the same metric space. For demonstration purpose, we sample -two Gaussian distributions in 2- and 3-dimensional spaces. - - -.. code-block:: default - - - - n_samples = 30 # nb samples - - mu_s = np.array([0, 0]) - cov_s = np.array([[1, 0], [0, 1]]) - - mu_t = np.array([4, 4, 4]) - cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - - xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) - P = sp.linalg.sqrtm(cov_t) - xt = np.random.randn(n_samples, 3).dot(P) + mu_t - - - - - - - - -Plotting the distributions --------------------------- - - -.. code-block:: default - - - - fig = pl.figure() - ax1 = fig.add_subplot(121) - ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - ax2 = fig.add_subplot(122, projection='3d') - ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_gromov.py:56: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Compute distance kernels, normalize them and then display ---------------------------------------------------------- - - -.. code-block:: default - - - - C1 = sp.spatial.distance.cdist(xs, xs) - C2 = sp.spatial.distance.cdist(xt, xt) - - C1 /= C1.max() - C2 /= C2.max() - - pl.figure() - pl.subplot(121) - pl.imshow(C1) - pl.subplot(122) - pl.imshow(C2) - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_gromov.py:75: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Compute Gromov-Wasserstein plans and distance ---------------------------------------------- - - -.. code-block:: default - - - p = ot.unif(n_samples) - q = ot.unif(n_samples) - - gw0, log0 = ot.gromov.gromov_wasserstein( - C1, C2, p, q, 'square_loss', verbose=True, log=True) - - gw, log = ot.gromov.entropic_gromov_wasserstein( - C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) - - - print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) - print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) - - - pl.figure(1, (10, 5)) - - pl.subplot(1, 2, 1) - pl.imshow(gw0, cmap='jet') - pl.title('Gromov Wasserstein') - - pl.subplot(1, 2, 2) - pl.imshow(gw, cmap='jet') - pl.title('Entropic Gromov Wasserstein') - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|8.019265e-02|0.000000e+00|0.000000e+00 - 1|3.734805e-02|1.147171e+00|4.284460e-02 - 2|2.923853e-02|2.773572e-01|8.109516e-03 - 3|2.478957e-02|1.794691e-01|4.448961e-03 - 4|2.444720e-02|1.400444e-02|3.423693e-04 - 5|2.444720e-02|0.000000e+00|0.000000e+00 - It. |Err - ------------------- - 0|8.259147e-02| - 10|6.113732e-04| - 20|1.650651e-08| - 30|5.671192e-12| - Gromov-Wasserstein distances: 0.024447198979060496 - Entropic Gromov-Wasserstein distances: 0.02488439679981518 - /home/rflamary/PYTHON/POT/examples/plot_gromov.py:106: 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 0.999 seconds) - - -.. _sphx_glr_download_auto_examples_plot_gromov.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_gromov.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_gromov.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3