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_L1_vs_L2.py | 208 -------------------------- 1 file changed, 208 deletions(-) delete mode 100644 docs/source/auto_examples/plot_OT_L1_vs_L2.py (limited to 'docs/source/auto_examples/plot_OT_L1_vs_L2.py') diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py deleted file mode 100644 index 37b429f..0000000 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ /dev/null @@ -1,208 +0,0 @@ -# -*- coding: utf-8 -*- -""" -========================================== -2D Optimal transport for different metrics -========================================== - -2D OT on empirical distributio with different gound metric. - -Stole the figure idea from Fig. 1 and 2 in -https://arxiv.org/pdf/1706.07650.pdf - - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# Dataset 1 : uniform sampling -# ---------------------------- - -n = 20 # nb samples -xs = np.zeros((n, 2)) -xs[:, 0] = np.arange(n) + 1 -xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... - -xt = np.zeros((n, 2)) -xt[:, 1] = np.arange(n) + 1 - -a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - -# loss matrix -M1 = ot.dist(xs, xt, metric='euclidean') -M1 /= M1.max() - -# loss matrix -M2 = ot.dist(xs, xt, metric='sqeuclidean') -M2 /= M2.max() - -# loss matrix -Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) -Mp /= Mp.max() - -# Data -pl.figure(1, figsize=(7, 3)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -pl.title('Source and target distributions') - - -# Cost matrices -pl.figure(2, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -pl.imshow(M1, interpolation='nearest') -pl.title('Euclidean cost') - -pl.subplot(1, 3, 2) -pl.imshow(M2, interpolation='nearest') -pl.title('Squared Euclidean cost') - -pl.subplot(1, 3, 3) -pl.imshow(Mp, interpolation='nearest') -pl.title('Sqrt Euclidean cost') -pl.tight_layout() - -############################################################################## -# Dataset 1 : Plot OT Matrices -# ---------------------------- - - -#%% EMD -G1 = ot.emd(a, b, M1) -G2 = ot.emd(a, b, M2) -Gp = ot.emd(a, b, Mp) - -# OT matrices -pl.figure(3, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -ot.plot.plot2D_samples_mat(xs, xt, G1, 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.axis('equal') -# pl.legend(loc=0) -pl.title('OT Euclidean') - -pl.subplot(1, 3, 2) -ot.plot.plot2D_samples_mat(xs, xt, G2, 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.axis('equal') -# pl.legend(loc=0) -pl.title('OT squared Euclidean') - -pl.subplot(1, 3, 3) -ot.plot.plot2D_samples_mat(xs, xt, Gp, 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.axis('equal') -# pl.legend(loc=0) -pl.title('OT sqrt Euclidean') -pl.tight_layout() - -pl.show() - - -############################################################################## -# Dataset 2 : Partial circle -# -------------------------- - -n = 50 # nb samples -xtot = np.zeros((n + 1, 2)) -xtot[:, 0] = np.cos( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) -xtot[:, 1] = np.sin( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - -xs = xtot[:n, :] -xt = xtot[1:, :] - -a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - -# loss matrix -M1 = ot.dist(xs, xt, metric='euclidean') -M1 /= M1.max() - -# loss matrix -M2 = ot.dist(xs, xt, metric='sqeuclidean') -M2 /= M2.max() - -# loss matrix -Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) -Mp /= Mp.max() - - -# Data -pl.figure(4, figsize=(7, 3)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -pl.title('Source and traget distributions') - - -# Cost matrices -pl.figure(5, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -pl.imshow(M1, interpolation='nearest') -pl.title('Euclidean cost') - -pl.subplot(1, 3, 2) -pl.imshow(M2, interpolation='nearest') -pl.title('Squared Euclidean cost') - -pl.subplot(1, 3, 3) -pl.imshow(Mp, interpolation='nearest') -pl.title('Sqrt Euclidean cost') -pl.tight_layout() - -############################################################################## -# Dataset 2 : Plot OT Matrices -# ----------------------------- - - -#%% EMD -G1 = ot.emd(a, b, M1) -G2 = ot.emd(a, b, M2) -Gp = ot.emd(a, b, Mp) - -# OT matrices -pl.figure(6, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -ot.plot.plot2D_samples_mat(xs, xt, G1, 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.axis('equal') -# pl.legend(loc=0) -pl.title('OT Euclidean') - -pl.subplot(1, 3, 2) -ot.plot.plot2D_samples_mat(xs, xt, G2, 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.axis('equal') -# pl.legend(loc=0) -pl.title('OT squared Euclidean') - -pl.subplot(1, 3, 3) -ot.plot.plot2D_samples_mat(xs, xt, Gp, 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.axis('equal') -# pl.legend(loc=0) -pl.title('OT sqrt Euclidean') -pl.tight_layout() - -pl.show() -- cgit v1.2.3