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diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst deleted file mode 100644 index 3ed4e11..0000000 --- a/docs/source/auto_examples/plot_gromov.rst +++ /dev/null @@ -1,214 +0,0 @@ - - -.. _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:: python - - - # Author: Erwan Vautier <erwan.vautier@gmail.com> - # Nicolas Courty <ncourty@irisa.fr> - # - # 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:: python - - - - 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:: python - - - - 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 - :align: center - - - - -Compute distance kernels, normalize them and then display ---------------------------------------------------------- - - - -.. code-block:: python - - - - 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 - :align: center - - - - -Compute Gromov-Wasserstein plans and distance ---------------------------------------------- - - - -.. code-block:: python - - - 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 - :align: center - - -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|4.328711e-02|0.000000e+00 - 1|2.281369e-02|-8.974178e-01 - 2|1.843659e-02|-2.374139e-01 - 3|1.602820e-02|-1.502598e-01 - 4|1.353712e-02|-1.840179e-01 - 5|1.285687e-02|-5.290977e-02 - 6|1.284537e-02|-8.952931e-04 - 7|1.284525e-02|-8.989584e-06 - 8|1.284525e-02|-8.989950e-08 - 9|1.284525e-02|-8.989949e-10 - It. |Err - ------------------- - 0|7.263293e-02| - 10|1.737784e-02| - 20|7.783978e-03| - 30|3.399419e-07| - 40|3.751207e-11| - Gromov-Wasserstein distances: 0.012845252089244688 - Entropic Gromov-Wasserstein distances: 0.013543882352191079 - - -**Total running time of the script:** ( 0 minutes 1.916 seconds) - - - -.. only :: html - - .. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_gromov.py <plot_gromov.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_gromov.ipynb <plot_gromov.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |