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diff --git a/docs/source/auto_examples/plot_otda_laplacian.py b/docs/source/auto_examples/plot_otda_laplacian.py
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--- a/docs/source/auto_examples/plot_otda_laplacian.py
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-# -*- coding: utf-8 -*-
-"""
-======================================================
-OT with Laplacian regularization for domain adaptation
-======================================================
-
-This example introduces a domain adaptation in a 2D setting and OTDA
-approach with Laplacian regularization.
-
-"""
-
-# Authors: Ievgen Redko <ievgen.redko@univ-st-etienne.fr>
-
-# 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=.01)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
-# EMD Transport with Laplacian regularization
-ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)
-ot_emd_laplace.fit(Xs=Xs, 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_emd_laplace = ot_emd_laplace.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, 3, 1)
-pl.imshow(ot_emd.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nEMDTransport')
-
-pl.figure(2, figsize=(15, 8))
-pl.subplot(2, 3, 2)
-pl.imshow(ot_sinkhorn.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornTransport')
-
-pl.subplot(2, 3, 3)
-pl.imshow(ot_emd_laplace.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nEMDLaplaceTransport')
-
-pl.subplot(2, 3, 4)
-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, 3, 5)
-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, 3, 6)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.xticks([])
-pl.yticks([])
-pl.title('Transported samples\nEMDLaplaceTransport')
-pl.tight_layout()
-
-pl.show()