From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:48:37 +0200 Subject: [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 --- docs/source/auto_examples/plot_OT_2D_samples.py | 128 ------------------------ 1 file changed, 128 deletions(-) delete mode 100644 docs/source/auto_examples/plot_OT_2D_samples.py (limited to 'docs/source/auto_examples/plot_OT_2D_samples.py') diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py deleted file mode 100644 index 63126ba..0000000 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ /dev/null @@ -1,128 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================== -2D Optimal transport between empirical distributions -==================================================== - -Illustration of 2D optimal transport between discributions that are weighted -sum of diracs. The OT matrix is plotted with the samples. - -""" - -# Author: Remi Flamary -# Kilian Fatras -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# Generate data -# ------------- - -#%% parameters and data generation - -n = 50 # nb samples - -mu_s = np.array([0, 0]) -cov_s = np.array([[1, 0], [0, 1]]) - -mu_t = np.array([4, 4]) -cov_t = np.array([[1, -.8], [-.8, 1]]) - -xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) -xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) - -a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples - -# loss matrix -M = ot.dist(xs, xt) -M /= M.max() - -############################################################################## -# Plot data -# --------- - -#%% plot samples - -pl.figure(1) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - -pl.figure(2) -pl.imshow(M, interpolation='nearest') -pl.title('Cost matrix M') - -############################################################################## -# Compute EMD -# ----------- - -#%% EMD - -G0 = ot.emd(a, b, M) - -pl.figure(3) -pl.imshow(G0, interpolation='nearest') -pl.title('OT matrix G0') - -pl.figure(4) -ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix with samples') - - -############################################################################## -# Compute Sinkhorn -# ---------------- - -#%% sinkhorn - -# reg term -lambd = 1e-3 - -Gs = ot.sinkhorn(a, b, M, lambd) - -pl.figure(5) -pl.imshow(Gs, interpolation='nearest') -pl.title('OT matrix sinkhorn') - -pl.figure(6) -ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix Sinkhorn with samples') - -pl.show() - - -############################################################################## -# Emprirical Sinkhorn -# ---------------- - -#%% sinkhorn - -# reg term -lambd = 1e-3 - -Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) - -pl.figure(7) -pl.imshow(Ges, interpolation='nearest') -pl.title('OT matrix empirical sinkhorn') - -pl.figure(8) -ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix Sinkhorn from samples') - -pl.show() -- cgit v1.2.3