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author | RĂ©mi Flamary <remi.flamary@gmail.com> | 2020-04-21 17:48:37 +0200 |
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committer | GitHub <noreply@github.com> | 2020-04-21 17:48:37 +0200 |
commit | a303cc6b483d3cd958c399621e22e40574bcbbc8 (patch) | |
tree | dea049cb692020462da8f00d9e117f93b839bb55 /docs/source/auto_examples/plot_screenkhorn_1D.py | |
parent | 0b2d808aaebb1cab60a272ea7901d5f77df43a9f (diff) |
[MRG] Actually run sphinx-gallery (#146)
* generate gallery
* remove mock
* add sklearn to requirermnt?txt for example
* remove latex from fgw example
* add networks for graph example
* remove all
* add requirement.txt rtd
* rtd debug
* update readme
* eradthedoc with redirection
* add conf rtd
Diffstat (limited to 'docs/source/auto_examples/plot_screenkhorn_1D.py')
-rw-r--r-- | docs/source/auto_examples/plot_screenkhorn_1D.py | 68 |
1 files changed, 0 insertions, 68 deletions
diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.py b/docs/source/auto_examples/plot_screenkhorn_1D.py deleted file mode 100644 index 840ead8..0000000 --- a/docs/source/auto_examples/plot_screenkhorn_1D.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================== -1D Screened optimal transport -=============================== - -This example illustrates the computation of Screenkhorn: -Screening Sinkhorn Algorithm for Optimal transport. -""" - -# Author: Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com> -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot.plot -from ot.datasets import make_1D_gauss as gauss -from ot.bregman import screenkhorn - -############################################################################## -# Generate data -# ------------- - -#%% 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 -# ---------------------------------- - -#%% 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') - -############################################################################## -# Solve Screenkhorn -# ----------------------- - -# Screenkhorn -lambd = 2e-03 # entropy parameter -ns_budget = 30 # budget number of points to be keeped in the source distribution -nt_budget = 30 # budget number of points to be keeped in the target distribution - -G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') -pl.show() |