This example illustrates the computation of Smooth and Sparse (KL an L2 reg.) OT and sparsity-constrained OT, together with their visualizations.
# Author: Remi Flamary <remi.flamary@unice.fr> # # License: MIT License # sphinx_gallery_thumbnail_number = 5 import numpy as np import matplotlib.pylab as pl import ot import ot.plot from ot.datasets import make_1D_gauss as gaussGenerate data Plot distributions and loss matrix
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()
<matplotlib.legend.Legend object at 0x7f590d7a33d0>
(<Axes: >, <Axes: >, <Axes: >)Solve Smooth OT
lambd = 1e-1 max_nz = 2 # two non-zero entries are permitted per column of the OT plan Gsc = ot.smooth.smooth_ot_dual( a, b, M, lambd, reg_type="sparsity_constrained", max_nz=max_nz ) pl.figure(5, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, Gsc, "Sparsity constrained OT matrix; k=2.") pl.show()
Total running time of the script: (0 minutes 0.649 seconds)
Gallery generated by Sphinx-Gallery
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