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diff --git a/examples/domain-adaptation/plot_otda_classes.py b/examples/domain-adaptation/plot_otda_classes.py
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+# -*- coding: utf-8 -*-
+"""
+========================
+OT for domain adaptation
+========================
+
+This example introduces a domain adaptation in a 2D setting and the 4 OTDA
+approaches currently supported in POT.
+
+"""
+
+# Authors: Remi Flamary <remi.flamary@unice.fr>
+#          Stanislas Chambon <stan.chambon@gmail.com>
+#
+# License: MIT License
+
+import matplotlib.pylab as pl
+import ot
+
+##############################################################################
+# Generate data
+# -------------
+
+n_source_samples = 150
+n_target_samples = 150
+
+Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)
+Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)
+
+
+##############################################################################
+# Instantiate the different transport algorithms and fit them
+# -----------------------------------------------------------
+
+# EMD Transport
+ot_emd = ot.da.EMDTransport()
+ot_emd.fit(Xs=Xs, Xt=Xt)
+
+# Sinkhorn Transport
+ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
+ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
+
+# Sinkhorn Transport with Group lasso regularization
+ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)
+ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)
+
+# Sinkhorn Transport with Group lasso regularization l1l2
+ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,
+                                      verbose=True)
+ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)
+
+# transport source samples onto target samples
+transp_Xs_emd = ot_emd.transform(Xs=Xs)
+transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
+transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)
+transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)
+
+
+##############################################################################
+# Fig 1 : plots source and target samples
+# ---------------------------------------
+
+pl.figure(1, figsize=(10, 5))
+pl.subplot(1, 2, 1)
+pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
+pl.xticks([])
+pl.yticks([])
+pl.legend(loc=0)
+pl.title('Source  samples')
+
+pl.subplot(1, 2, 2)
+pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
+pl.xticks([])
+pl.yticks([])
+pl.legend(loc=0)
+pl.title('Target samples')
+pl.tight_layout()
+
+
+##############################################################################
+# Fig 2 : plot optimal couplings and transported samples
+# ------------------------------------------------------
+
+param_img = {'interpolation': 'nearest'}
+
+pl.figure(2, figsize=(15, 8))
+pl.subplot(2, 4, 1)
+pl.imshow(ot_emd.coupling_, **param_img)
+pl.xticks([])
+pl.yticks([])
+pl.title('Optimal coupling\nEMDTransport')
+
+pl.subplot(2, 4, 2)
+pl.imshow(ot_sinkhorn.coupling_, **param_img)
+pl.xticks([])
+pl.yticks([])
+pl.title('Optimal coupling\nSinkhornTransport')
+
+pl.subplot(2, 4, 3)
+pl.imshow(ot_lpl1.coupling_, **param_img)
+pl.xticks([])
+pl.yticks([])
+pl.title('Optimal coupling\nSinkhornLpl1Transport')
+
+pl.subplot(2, 4, 4)
+pl.imshow(ot_l1l2.coupling_, **param_img)
+pl.xticks([])
+pl.yticks([])
+pl.title('Optimal coupling\nSinkhornL1l2Transport')
+
+pl.subplot(2, 4, 5)
+pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.xticks([])
+pl.yticks([])
+pl.title('Transported samples\nEmdTransport')
+pl.legend(loc="lower left")
+
+pl.subplot(2, 4, 6)
+pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.xticks([])
+pl.yticks([])
+pl.title('Transported samples\nSinkhornTransport')
+
+pl.subplot(2, 4, 7)
+pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.xticks([])
+pl.yticks([])
+pl.title('Transported samples\nSinkhornLpl1Transport')
+
+pl.subplot(2, 4, 8)
+pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.xticks([])
+pl.yticks([])
+pl.title('Transported samples\nSinkhornL1l2Transport')
+pl.tight_layout()
+
+pl.show()