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diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py
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-# -*- coding: utf-8 -*-
-"""
-===============================================
-OT mapping estimation for domain adaptation [8]
-===============================================
-
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
-    "Mapping estimation for discrete optimal transport",
-    Neural Information Processing Systems (NIPS), 2016.
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-#%% dataset generation
-
-np.random.seed(0)  # makes example reproducible
-
-n = 100  # nb samples in source and target datasets
-theta = 2 * np.pi / 20
-nz = 0.1
-xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz)
-xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz)
-
-# one of the target mode changes its variance (no linear mapping)
-xt[yt == 2] *= 3
-xt = xt + 4
-
-
-#%% plot samples
-
-pl.figure(1, (6.4, 3))
-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')
-
-
-#%% OT linear mapping estimation
-
-eta = 1e-8   # quadratic regularization for regression
-mu = 1e0     # weight of the OT linear term
-bias = True  # estimate a bias
-
-ot_mapping = ot.da.OTDA_mapping_linear()
-ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True)
-
-xst = ot_mapping.predict(xs)  # use the estimated mapping
-xst0 = ot_mapping.interp()   # use barycentric mapping
-
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 2, 1)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=.3)
-pl.scatter(xst0[:, 0], xst0[:, 1], c=ys,
-           marker='+', label='barycentric mapping')
-pl.title("barycentric mapping")
-
-pl.subplot(2, 2, 2)
-pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=.3)
-pl.scatter(xst[:, 0], xst[:, 1], c=ys, marker='+', label='Learned mapping')
-pl.title("Learned mapping")
-pl.tight_layout()
-
-#%% Kernel mapping estimation
-
-eta = 1e-5   # quadratic regularization for regression
-mu = 1e-1     # weight of the OT linear term
-bias = True  # estimate a bias
-sigma = 1    # sigma bandwidth fot gaussian kernel
-
-
-ot_mapping_kernel = ot.da.OTDA_mapping_kernel()
-ot_mapping_kernel.fit(
-    xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True)
-
-xst_kernel = ot_mapping_kernel.predict(xs)  # use the estimated mapping
-xst0_kernel = ot_mapping_kernel.interp()   # use barycentric mapping
-
-
-#%% Plotting the mapped 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(xst0[:, 0], xst0[:, 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(xst[:, 0], xst[:, 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(xst0_kernel[:, 0], xst0_kernel[:, 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(xst_kernel[:, 0], xst_kernel[:, 1], c=ys,
-           marker='+', label='Learned mapping')
-pl.title("Estim. mapping (kernel)")
-pl.tight_layout()
-
-pl.show()