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diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py
deleted file mode 100644
index 53e4bae..0000000
--- a/examples/plot_OTDA_classes.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-========================
-OT for domain adaptation
-========================
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import matplotlib.pylab as pl
-import ot
-
-
-#%% parameters
-
-n = 150  # nb samples in source and target datasets
-
-xs, ys = ot.datasets.get_data_classif('3gauss', n)
-xt, yt = ot.datasets.get_data_classif('3gauss2', n)
-
-
-#%% plot samples
-
-pl.figure(1, figsize=(6.4, 3))
-
-pl.subplot(1, 2, 1)
-pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.legend(loc=0)
-pl.title('Source  distributions')
-
-pl.subplot(1, 2, 2)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.legend(loc=0)
-pl.title('target  distributions')
-
-
-#%% OT estimation
-
-# LP problem
-da_emd = ot.da.OTDA()     # init class
-da_emd.fit(xs, xt)       # fit distributions
-xst0 = da_emd.interp()    # interpolation of source samples
-
-# sinkhorn regularization
-lambd = 1e-1
-da_entrop = ot.da.OTDA_sinkhorn()
-da_entrop.fit(xs, xt, reg=lambd)
-xsts = da_entrop.interp()
-
-# non-convex Group lasso regularization
-reg = 1e-1
-eta = 1e0
-da_lpl1 = ot.da.OTDA_lpl1()
-da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta)
-xstg = da_lpl1.interp()
-
-# True Group lasso regularization
-reg = 1e-1
-eta = 2e0
-da_l1l2 = ot.da.OTDA_l1l2()
-da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True)
-xstgl = da_l1l2.interp()
-
-#%% plot interpolated source samples
-
-param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}
-
-pl.figure(2, figsize=(8, 4.5))
-pl.subplot(2, 4, 1)
-pl.imshow(da_emd.G, **param_img)
-pl.title('OT matrix')
-
-pl.subplot(2, 4, 2)
-pl.imshow(da_entrop.G, **param_img)
-pl.title('OT matrix\nsinkhorn')
-
-pl.subplot(2, 4, 3)
-pl.imshow(da_lpl1.G, **param_img)
-pl.title('OT matrix\nnon-convex Group Lasso')
-
-pl.subplot(2, 4, 4)
-pl.imshow(da_l1l2.G, **param_img)
-pl.title('OT matrix\nGroup Lasso')
-
-pl.subplot(2, 4, 5)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(xst0[:, 0], xst0[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Interp samples')
-pl.legend(loc=0)
-
-pl.subplot(2, 4, 6)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(xsts[:, 0], xsts[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Interp samples\nSinkhorn')
-
-pl.subplot(2, 4, 7)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(xstg[:, 0], xstg[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Interp samples\nnon-convex Group Lasso')
-
-pl.subplot(2, 4, 8)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(xstgl[:, 0], xstgl[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Interp samples\nGroup Lasso')
-pl.tight_layout()
-pl.show()