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diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py
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-# -*- 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()