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author | Gard Spreemann <gspr@nonempty.org> | 2020-07-09 08:50:11 +0200 |
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committer | Gard Spreemann <gspr@nonempty.org> | 2020-07-09 08:50:11 +0200 |
commit | 616c6039614e96b6cfd88eb7db25ce11c7302c30 (patch) | |
tree | a745ec24604f660b256dbaee214affa497c9c21e /docs/source/auto_examples/plot_UOT_1D.rst | |
parent | d62f05daf348b4e554056f298c66cbd64f5e3c6e (diff) | |
parent | a16b9471d7114ec08977479b7249efe747702b97 (diff) |
Merge branch 'dfsg/latest' into debian/sid
Diffstat (limited to 'docs/source/auto_examples/plot_UOT_1D.rst')
-rw-r--r-- | docs/source/auto_examples/plot_UOT_1D.rst | 173 |
1 files changed, 0 insertions, 173 deletions
diff --git a/docs/source/auto_examples/plot_UOT_1D.rst b/docs/source/auto_examples/plot_UOT_1D.rst deleted file mode 100644 index 8e618b4..0000000 --- a/docs/source/auto_examples/plot_UOT_1D.rst +++ /dev/null @@ -1,173 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_UOT_1D.py: - - -=============================== -1D Unbalanced optimal transport -=============================== - -This example illustrates the computation of Unbalanced Optimal transport -using a Kullback-Leibler relaxation. - - - -.. code-block:: python - - - # Author: Hicham Janati <hicham.janati@inria.fr> - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - from ot.datasets import make_1D_gauss as gauss - - - - - - - -Generate data -------------- - - - -.. code-block:: python - - - - #%% parameters - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a = gauss(n, m=20, s=5) # m= mean, s= std - b = gauss(n, m=60, s=10) - - # make distributions unbalanced - b *= 5. - - # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) - M /= M.max() - - - - - - - - -Plot distributions and loss matrix ----------------------------------- - - - -.. code-block:: python - - - #%% plot the distributions - - pl.figure(1, figsize=(6.4, 3)) - pl.plot(x, a, 'b', label='Source distribution') - pl.plot(x, b, 'r', label='Target distribution') - pl.legend() - - # plot distributions and loss matrix - - pl.figure(2, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_002.png - :scale: 47 - - - - -Solve Unbalanced Sinkhorn --------------- - - - -.. code-block:: python - - - - # Sinkhorn - - epsilon = 0.1 # entropy parameter - alpha = 1. # Unbalanced KL relaxation parameter - Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_006.png - :align: center - - -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Err - ------------------- - 0|1.838786e+00| - 10|1.242379e-01| - 20|2.581314e-03| - 30|5.674552e-05| - 40|1.252959e-06| - 50|2.768136e-08| - 60|6.116090e-10| - - -**Total running time of the script:** ( 0 minutes 0.259 seconds) - - - -.. only :: html - - .. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_UOT_1D.py <plot_UOT_1D.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_UOT_1D.ipynb <plot_UOT_1D.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |