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diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py
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+# -*- coding: utf-8 -*-
+"""
+===============================================
+OT mapping estimation for domain adaptation [8]
+===============================================
+
+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.get_data_classif(
+    'gaussrot', n_source_samples, nz=noise_level)
+Xs_new, _ = ot.datasets.get_data_classif(
+    'gaussrot', n_source_samples, nz=noise_level)
+Xt, yt = ot.datasets.get_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
+
+
+##############################################################################
+# 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 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')
+
+
+##############################################################################
+# 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()