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_optim_OTreg.py | 129 -------------------------- 1 file changed, 129 deletions(-) delete mode 100644 docs/source/auto_examples/plot_optim_OTreg.py (limited to 'docs/source/auto_examples/plot_optim_OTreg.py') diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py deleted file mode 100644 index 2c58def..0000000 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ /dev/null @@ -1,129 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================== -Regularized OT with generic solver -================================== - -Illustrates the use of the generic solver for regularized OT with -user-designed regularization term. It uses Conditional gradient as in [6] and -generalized Conditional Gradient as proposed in [5][7]. - - -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for -Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine -Intelligence , vol.PP, no.99, pp.1-1. - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, -7(3), 1853-1882. - -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. -arXiv preprint arXiv:1510.06567. - - - -""" - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -b = ot.datasets.make_1D_gauss(n, m=60, s=10) - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -M /= M.max() - -############################################################################## -# Solve EMD -# --------- - -#%% EMD - -G0 = ot.emd(a, b, M) - -pl.figure(3, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - -############################################################################## -# Solve EMD with Frobenius norm regularization -# -------------------------------------------- - -#%% Example with Frobenius norm regularization - - -def f(G): - return 0.5 * np.sum(G**2) - - -def df(G): - return G - - -reg = 1e-1 - -Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) - -pl.figure(3) -ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') - -############################################################################## -# Solve EMD with entropic regularization -# -------------------------------------- - -#%% Example with entropic regularization - - -def f(G): - return np.sum(G * np.log(G)) - - -def df(G): - return np.log(G) + 1. - - -reg = 1e-3 - -Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') - -############################################################################## -# Solve EMD with Frobenius norm + entropic regularization -# ------------------------------------------------------- - -#%% Example with Frobenius norm + entropic regularization with gcg - - -def f(G): - return 0.5 * np.sum(G**2) - - -def df(G): - return G - - -reg1 = 1e-3 -reg2 = 1e-1 - -Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) - -pl.figure(5, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') -pl.show() -- cgit v1.2.3