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diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst deleted file mode 100644 index 27bca2c..0000000 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ /dev/null @@ -1,189 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_compute_emd.py: - - -================= -Plot multiple EMD -================= - -Shows how to compute multiple EMD and Sinkhorn with two differnt -ground metrics and plot their values for diffeent distributions. - - - - - -.. code-block:: python - - - # Author: Remi Flamary <remi.flamary@unice.fr> - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - from ot.datasets import make_1D_gauss as gauss - - - - - - - - -Generate data -------------- - - - -.. code-block:: python - - - #%% parameters - - n = 100 # nb bins - n_target = 50 # nb target distributions - - - # bin positions - x = np.arange(n, dtype=np.float64) - - lst_m = np.linspace(20, 90, n_target) - - # Gaussian distributions - a = gauss(n, m=20, s=5) # m= mean, s= std - - B = np.zeros((n, n_target)) - - for i, m in enumerate(lst_m): - B[:, i] = gauss(n, m=m, s=5) - - # loss matrix and normalization - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') - M /= M.max() - M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') - M2 /= M2.max() - - - - - - - -Plot data ---------- - - - -.. code-block:: python - - - #%% plot the distributions - - pl.figure(1) - pl.subplot(2, 1, 1) - pl.plot(x, a, 'b', label='Source distribution') - pl.title('Source distribution') - pl.subplot(2, 1, 2) - pl.plot(x, B, label='Target distributions') - pl.title('Target distributions') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_001.png - :align: center - - - - -Compute EMD for the different losses ------------------------------------- - - - -.. code-block:: python - - - #%% Compute and plot distributions and loss matrix - - d_emd = ot.emd2(a, B, M) # direct computation of EMD - d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2 - - - pl.figure(2) - pl.plot(d_emd, label='Euclidean EMD') - pl.plot(d_emd2, label='Squared Euclidean EMD') - pl.title('EMD distances') - pl.legend() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_003.png - :align: center - - - - -Compute Sinkhorn for the different losses ------------------------------------------ - - - -.. code-block:: python - - - #%% - reg = 1e-2 - d_sinkhorn = ot.sinkhorn2(a, B, M, reg) - d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg) - - pl.figure(2) - pl.clf() - pl.plot(d_emd, label='Euclidean EMD') - pl.plot(d_emd2, label='Squared Euclidean EMD') - pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn') - pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn') - pl.title('EMD distances') - pl.legend() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_004.png - :align: center - - - - -**Total running time of the script:** ( 0 minutes 0.446 seconds) - - - -.. only :: html - - .. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_compute_emd.py <plot_compute_emd.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_compute_emd.ipynb <plot_compute_emd.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |