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diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.rst b/docs/source/auto_examples/plot_OT_1D_smooth.rst deleted file mode 100644 index 5a0ebd3..0000000 --- a/docs/source/auto_examples/plot_OT_1D_smooth.rst +++ /dev/null @@ -1,242 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OT_1D_smooth.py: - - -=========================== -1D smooth optimal transport -=========================== - -This example illustrates the computation of EMD, Sinkhorn and smooth OT 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_smooth_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_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_smooth_005.png - :align: center - - - - -Solve Sinkhorn --------------- - - - -.. code-block:: python - - - - #%% Sinkhorn - - lambd = 2e-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_smooth_007.png - :align: center - - -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Err - ------------------- - 0|7.958844e-02| - 10|5.921715e-03| - 20|1.238266e-04| - 30|2.469780e-06| - 40|4.919966e-08| - 50|9.800197e-10| - - -Solve Smooth OT --------------- - - - -.. code-block:: python - - - - #%% Smooth OT with KL regularization - - lambd = 2e-3 - Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') - - pl.figure(5, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') - - pl.show() - - - #%% Smooth OT with KL regularization - - lambd = 1e-1 - Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') - - pl.figure(6, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') - - pl.show() - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_009.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_010.png - :scale: 47 - - - - -**Total running time of the script:** ( 0 minutes 1.053 seconds) - - - -.. only :: html - - .. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OT_1D_smooth.py <plot_OT_1D_smooth.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OT_1D_smooth.ipynb <plot_OT_1D_smooth.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |