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.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_plot_compute_emd.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
.. _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:: default
# 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:: default
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:: default
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
:class: sphx-glr-single-img
Compute EMD for the different losses
------------------------------------
.. code-block:: default
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_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<matplotlib.legend.Legend object at 0x7f4c7389f438>
Compute Sinkhorn for the different losses
-----------------------------------------
.. code-block:: default
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_003.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/home/rflamary/PYTHON/POT/examples/plot_compute_emd.py:102: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
pl.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.436 seconds)
.. _sphx_glr_download_auto_examples_plot_compute_emd.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_compute_emd.py <plot_compute_emd.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
: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.github.io>`_
|
