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diff --git a/examples/da/plot_otda_semi_supervised.py b/examples/da/plot_otda_semi_supervised.py
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-# -*- coding: utf-8 -*-
-"""
-============================================
-OTDA unsupervised vs semi-supervised setting
-============================================
-
-This example introduces a semi supervised domain adaptation in a 2D setting.
-It explicits the problem of semi supervised domain adaptation and introduces
-some optimal transport approaches to solve it.
-
-Quantities such as optimal couplings, greater coupling coefficients and
-transported samples are represented in order to give a visual understanding
-of what the transport methods are doing.
-"""
-
-# 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_samples_source = 150
-n_samples_target = 150
-
-Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)
-Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)
-
-
-##############################################################################
-# Transport source samples onto target samples
-##############################################################################
-
-# unsupervised domain adaptation
-ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)
-transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)
-
-# semi-supervised domain adaptation
-ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)
-transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)
-
-# semi supervised DA uses available labaled target samples to modify the cost
-# matrix involved in the OT problem. The cost of transporting a source sample
-# of class A onto a target sample of class B != A is set to infinite, or a
-# very large value
-
-# note that in the present case we consider that all the target samples are
-# labeled. For daily applications, some target sample might not have labels,
-# in this case the element of yt corresponding to these samples should be
-# filled with -1.
-
-# Warning: we recall that -1 cannot be used as a class label
-
-
-##############################################################################
-# Fig 1 : plots source and target samples + matrix of pairwise distance
-##############################################################################
-
-pl.figure(1, figsize=(10, 10))
-pl.subplot(2, 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(2, 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.subplot(2, 2, 3)
-pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Cost matrix - unsupervised DA')
-
-pl.subplot(2, 2, 4)
-pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Cost matrix - semisupervised DA')
-
-pl.tight_layout()
-
-# the optimal coupling in the semi-supervised DA case will exhibit " shape
-# similar" to the cost matrix, (block diagonal matrix)
-
-
-##############################################################################
-# Fig 2 : plots optimal couplings for the different methods
-##############################################################################
-
-pl.figure(2, figsize=(8, 4))
-
-pl.subplot(1, 2, 1)
-pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nUnsupervised DA')
-
-pl.subplot(1, 2, 2)
-pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSemi-supervised DA')
-
-pl.tight_layout()
-
-
-##############################################################################
-# Fig 3 : plot transported samples
-##############################################################################
-
-# display transported samples
-pl.figure(4, figsize=(8, 4))
-pl.subplot(1, 2, 1)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nEmdTransport')
-pl.legend(loc=0)
-pl.xticks([])
-pl.yticks([])
-
-pl.subplot(1, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nSinkhornTransport')
-pl.xticks([])
-pl.yticks([])
-
-pl.tight_layout()
-pl.show()