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author | Gard Spreemann <gspr@nonempty.org> | 2020-07-09 08:49:39 +0200 |
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committer | Gard Spreemann <gspr@nonempty.org> | 2020-07-09 08:49:39 +0200 |
commit | a16b9471d7114ec08977479b7249efe747702b97 (patch) | |
tree | 692f3061d0329cad954206b2bf903ba3384403f0 /docs/source/auto_examples/plot_OT_1D.rst | |
parent | 0812fcd82cbf11d444619e96c55ba507bc09ef5d (diff) | |
parent | 94d5c8cc9046854f473d8e4526a3bcf214eb5411 (diff) |
Merge tag '0.7.0' into dfsg/latest
Diffstat (limited to 'docs/source/auto_examples/plot_OT_1D.rst')
-rw-r--r-- | docs/source/auto_examples/plot_OT_1D.rst | 199 |
1 files changed, 0 insertions, 199 deletions
diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst deleted file mode 100644 index b97d67c..0000000 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ /dev/null @@ -1,199 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OT_1D.py: - - -==================== -1D optimal transport -==================== - -This example illustrates the computation of EMD and Sinkhorn transport plans -and their visualization. - - - - -.. code-block:: python - - - # Author: Remi Flamary <remi.flamary@unice.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) - - # 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_OT_1D_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png - :scale: 47 - - - - -Solve EMD ---------- - - - -.. code-block:: python - - - - #%% EMD - - G0 = ot.emd(a, b, M) - - pl.figure(3, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_005.png - :align: center - - - - -Solve Sinkhorn --------------- - - - -.. code-block:: python - - - - #%% Sinkhorn - - lambd = 1e-3 - Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_007.png - :align: center - - -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Err - ------------------- - 0|8.187970e-02| - 10|3.460174e-02| - 20|6.633335e-03| - 30|9.797798e-04| - 40|1.389606e-04| - 50|1.959016e-05| - 60|2.759079e-06| - 70|3.885166e-07| - 80|5.470605e-08| - 90|7.702918e-09| - 100|1.084609e-09| - 110|1.527180e-10| - - -**Total running time of the script:** ( 0 minutes 0.561 seconds) - - - -.. only :: html - - .. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OT_1D.py <plot_OT_1D.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OT_1D.ipynb <plot_OT_1D.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |