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diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py
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-# -*- coding: utf-8 -*-
-"""
-===========================================
-OT mapping estimation for domain adaptation
-===========================================
-
-This example presents how to use MappingTransport to estimate at the same
-time both the coupling transport and approximate the transport map with either
-a linear or a kernelized mapping as introduced in [8].
-
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
-    "Mapping estimation for discrete optimal transport",
-    Neural Information Processing Systems (NIPS), 2016.
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-#          Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-##############################################################################
-# Generate data
-# -------------
-
-n_source_samples = 100
-n_target_samples = 100
-theta = 2 * np.pi / 20
-noise_level = 0.1
-
-Xs, ys = ot.datasets.make_data_classif(
-    'gaussrot', n_source_samples, nz=noise_level)
-Xs_new, _ = ot.datasets.make_data_classif(
-    'gaussrot', n_source_samples, nz=noise_level)
-Xt, yt = ot.datasets.make_data_classif(
-    'gaussrot', n_target_samples, theta=theta, nz=noise_level)
-
-# one of the target mode changes its variance (no linear mapping)
-Xt[yt == 2] *= 3
-Xt = Xt + 4
-
-##############################################################################
-# Plot data
-# ---------
-
-pl.figure(1, (10, 5))
-pl.clf()
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.legend(loc=0)
-pl.title('Source and target distributions')
-
-
-##############################################################################
-# Instantiate the different transport algorithms and fit them
-# -----------------------------------------------------------
-
-# MappingTransport with linear kernel
-ot_mapping_linear = ot.da.MappingTransport(
-    kernel="linear", mu=1e0, eta=1e-8, bias=True,
-    max_iter=20, verbose=True)
-
-ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
-# for original source samples, transform applies barycentric mapping
-transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)
-
-# for out of source samples, transform applies the linear mapping
-transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)
-
-
-# MappingTransport with gaussian kernel
-ot_mapping_gaussian = ot.da.MappingTransport(
-    kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1,
-    max_iter=10, verbose=True)
-ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
-# for original source samples, transform applies barycentric mapping
-transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)
-
-# for out of source samples, transform applies the gaussian mapping
-transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)
-
-
-##############################################################################
-# Plot transported samples
-# ------------------------
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 2, 1)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',
-           label='Mapped source samples')
-pl.title("Bary. mapping (linear)")
-pl.legend(loc=0)
-
-pl.subplot(2, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],
-           c=ys, marker='+', label='Learned mapping')
-pl.title("Estim. mapping (linear)")
-
-pl.subplot(2, 2, 3)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,
-           marker='+', label='barycentric mapping')
-pl.title("Bary. mapping (kernel)")
-
-pl.subplot(2, 2, 4)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,
-           marker='+', label='Learned mapping')
-pl.title("Estim. mapping (kernel)")
-pl.tight_layout()
-
-pl.show()