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-rw-r--r--docs/source/auto_examples/plot_gromov.rst206
1 files changed, 106 insertions, 100 deletions
diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst
index 65cf4e4..ad29f7a 100644
--- a/docs/source/auto_examples/plot_gromov.rst
+++ b/docs/source/auto_examples/plot_gromov.rst
@@ -12,157 +12,160 @@ 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
-
-
+.. rst-class:: sphx-glr-horizontal
+ *
+ .. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png
+ :scale: 47
+ *
-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.
+ .. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png
+ :scale: 47
+.. rst-class:: sphx-glr-script-out
-.. 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.get_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
-
-
+ Out::
+ It. |Loss |Delta loss
+ --------------------------------
+ 0|4.042674e-02|0.000000e+00
+ 1|2.432476e-02|-6.619583e-01
+ 2|2.170023e-02|-1.209448e-01
+ 3|1.941223e-02|-1.178640e-01
+ 4|1.823606e-02|-6.449667e-02
+ 5|1.446641e-02|-2.605800e-01
+ 6|1.184011e-02|-2.218140e-01
+ 7|1.173274e-02|-9.150805e-03
+ 8|1.173127e-02|-1.253458e-04
+ 9|1.173126e-02|-1.256842e-06
+ 10|1.173126e-02|-1.256876e-08
+ 11|1.173126e-02|-1.256885e-10
+ It. |Err
+ -------------------
+ 0|7.034302e-02|
+ 10|1.044218e-03|
+ 20|5.426783e-08|
+ 30|3.532029e-12|
+ Gromov-Wasserstein distances: 0.0117312557987
+ Entropic Gromov-Wasserstein distances: 0.0101639418389
+|
-Plotting the distributions
---------------------------
+.. code-block:: python
-.. code-block:: python
+ # Author: Erwan Vautier <erwan.vautier@gmail.com>
+ # Nicolas Courty <ncourty@irisa.fr>
+ #
+ # License: MIT License
-
-
- 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()
-
-
+ 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.
-.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png
- :align: center
+ 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]])
-Compute distance kernels, normalize them and then display
----------------------------------------------------------
+ xs = ot.datasets.get_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
-.. code-block:: python
+ #
+ # Plotting the distributions
+ # --------------------------
-
-
- 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()
-
+ 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_002.png
- :align: center
+ #
+ # Compute distance kernels, normalize them and then display
+ # ---------------------------------------------------------
+ C1 = sp.spatial.distance.cdist(xs, xs)
+ C2 = sp.spatial.distance.cdist(xt, xt)
+ C1 /= C1.max()
+ C2 /= C2.max()
-Compute Gromov-Wasserstein plans and distance
----------------------------------------------
+ pl.figure()
+ pl.subplot(121)
+ pl.imshow(C1)
+ pl.subplot(122)
+ pl.imshow(C2)
+ pl.show()
+ #
+ # Compute Gromov-Wasserstein plans and distance
+ # ---------------------------------------------
+ p = ot.unif(n_samples)
+ q = ot.unif(n_samples)
-.. code-block:: python
+ gw0, log0 = ot.gromov.gromov_wasserstein(
+ C1, C2, p, q, 'square_loss', verbose=True, log=True)
-
-
- p = ot.unif(n_samples)
- q = ot.unif(n_samples)
-
- gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)
- gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)
-
- print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))
-
- pl.figure()
- pl.imshow(gw, cmap='jet')
- pl.colorbar()
- pl.show()
+ 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']))
-.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png
- :align: center
+ pl.figure(1, (10, 5))
-.. rst-class:: sphx-glr-script-out
+ pl.subplot(1, 2, 1)
+ pl.imshow(gw0, cmap='jet')
+ pl.title('Gromov Wasserstein')
- Out::
+ pl.subplot(1, 2, 2)
+ pl.imshow(gw, cmap='jet')
+ pl.title('Entropic Gromov Wasserstein')
- Gromov-Wasserstein distances between the distribution: 0.225058076974
+ pl.show()
+**Total running time of the script:** ( 0 minutes 1.465 seconds)
-**Total running time of the script:** ( 0 minutes 4.070 seconds)
+.. only :: html
-.. container:: sphx-glr-footer
+ .. container:: sphx-glr-footer
.. container:: sphx-glr-download
@@ -175,6 +178,9 @@ Compute Gromov-Wasserstein plans and distance
:download:`Download Jupyter notebook: plot_gromov.ipynb <plot_gromov.ipynb>`
-.. rst-class:: sphx-glr-signature
- `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-28 07:07:40 +0000