A RetroSearch Logo

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Search Query:

Showing content from https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html below:

Website Navigation


Smooth and sparse OT example — POT Python Optimal Transport 0.9.5 documentation

Smooth and sparse OT example

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 gauss
Generate 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


RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4