move da

6 files changed, 1 insertions, 786 deletions

diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py
deleted file mode 100644
index ec57a37..0000000
--- a/examples/da/plot_otda_classes.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# -*- 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.get_data_classif('3gauss', n_source_samples)
-Xt, yt = ot.datasets.get_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', 'cmap': 'spectral'}
-
-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()
diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py
deleted file mode 100644
index 3984afb..0000000
--- a/examples/da/plot_otda_color_images.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-========================================================
-OT for domain adaptation with image color adaptation [6]
-========================================================
-
-This example presents a way of transferring colors between two image
-with Optimal Transport as introduced in [6]
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
-Regularized discrete optimal transport.
-SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-#          Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-from scipy import ndimage
-import matplotlib.pylab as pl
-import ot
-
-
-r = np.random.RandomState(42)
-
-
-def im2mat(I):
-    """Converts and image to matrix (one pixel per line)"""
-    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
-def mat2im(X, shape):
-    """Converts back a matrix to an image"""
-    return X.reshape(shape)
-
-
-def minmax(I):
-    return np.clip(I, 0, 1)
-
-
-##############################################################################
-# generate data
-##############################################################################
-
-# Loading images
-I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-X1 = im2mat(I1)
-X2 = im2mat(I2)
-
-# training samples
-nb = 1000
-idx1 = r.randint(X1.shape[0], size=(nb,))
-idx2 = r.randint(X2.shape[0], size=(nb,))
-
-Xs = X1[idx1, :]
-Xt = X2[idx2, :]
-
-
-##############################################################################
-# Instantiate the different transport algorithms and fit them
-##############################################################################
-
-# EMDTransport
-ot_emd = ot.da.EMDTransport()
-ot_emd.fit(Xs=Xs, Xt=Xt)
-
-# SinkhornTransport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
-# prediction between images (using out of sample prediction as in [6])
-transp_Xs_emd = ot_emd.transform(Xs=X1)
-transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)
-
-transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)
-transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)
-
-I1t = minmax(mat2im(transp_Xs_emd, I1.shape))
-I2t = minmax(mat2im(transp_Xt_emd, I2.shape))
-
-I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
-I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))
-
-
-##############################################################################
-# plot original image
-##############################################################################
-
-pl.figure(1, figsize=(6.4, 3))
-
-pl.subplot(1, 2, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Image 2')
-
-
-##############################################################################
-# scatter plot of colors
-##############################################################################
-
-pl.figure(2, figsize=(6.4, 3))
-
-pl.subplot(1, 2, 1)
-pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 2')
-pl.tight_layout()
-
-
-##############################################################################
-# plot new images
-##############################################################################
-
-pl.figure(3, figsize=(8, 4))
-
-pl.subplot(2, 3, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(2, 3, 2)
-pl.imshow(I1t)
-pl.axis('off')
-pl.title('Image 1 Adapt')
-
-pl.subplot(2, 3, 3)
-pl.imshow(I1te)
-pl.axis('off')
-pl.title('Image 1 Adapt (reg)')
-
-pl.subplot(2, 3, 4)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Image 2')
-
-pl.subplot(2, 3, 5)
-pl.imshow(I2t)
-pl.axis('off')
-pl.title('Image 2 Adapt')
-
-pl.subplot(2, 3, 6)
-pl.imshow(I2te)
-pl.axis('off')
-pl.title('Image 2 Adapt (reg)')
-pl.tight_layout()
-
-pl.show()
diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py
deleted file mode 100644
index 3daa0a6..0000000
--- a/examples/da/plot_otda_d2.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==============================
-OT for empirical distributions
-==============================
-
-This example introduces a domain adaptation in a 2D setting. It explicits
-the problem of 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)
-
-# Cost matrix
-M = ot.dist(Xs, Xt, metric='sqeuclidean')
-
-
-##############################################################################
-# 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)
-
-# 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)
-
-
-##############################################################################
-# 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(M, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Matrix of pairwise distances')
-pl.tight_layout()
-
-
-##############################################################################
-# Fig 2 : plots optimal couplings for the different methods
-##############################################################################
-
-pl.figure(2, figsize=(10, 6))
-
-pl.subplot(2, 3, 1)
-pl.imshow(ot_emd.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nEMDTransport')
-
-pl.subplot(2, 3, 2)
-pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornTransport')
-
-pl.subplot(2, 3, 3)
-pl.imshow(ot_lpl1.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornLpl1Transport')
-
-pl.subplot(2, 3, 4)
-ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])
-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.xticks([])
-pl.yticks([])
-pl.title('Main coupling coefficients\nEMDTransport')
-
-pl.subplot(2, 3, 5)
-ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])
-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.xticks([])
-pl.yticks([])
-pl.title('Main coupling coefficients\nSinkhornTransport')
-
-pl.subplot(2, 3, 6)
-ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])
-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.xticks([])
-pl.yticks([])
-pl.title('Main coupling coefficients\nSinkhornLpl1Transport')
-pl.tight_layout()
-
-
-##############################################################################
-# Fig 3 : plot transported samples
-##############################################################################
-
-# display transported samples
-pl.figure(4, figsize=(10, 4))
-pl.subplot(1, 3, 1)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 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, 3, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nSinkhornTransport')
-pl.xticks([])
-pl.yticks([])
-
-pl.subplot(1, 3, 3)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nSinkhornLpl1Transport')
-pl.xticks([])
-pl.yticks([])
-
-pl.tight_layout()
-pl.show()
diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py
deleted file mode 100644
index 09d2cb4..0000000
--- a/examples/da/plot_otda_mapping.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-===============================================
-OT mapping estimation for domain adaptation [8]
-===============================================
-
-This example presents how to use MappingTransport to estimate at the same
-time both the coupling transport and approximate the transport map with either
-a linear or a kernelized mapping as introduced in [8]
-
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
-    "Mapping estimation for discrete optimal transport",
-    Neural Information Processing Systems (NIPS), 2016.
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-#          Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-##############################################################################
-# generate data
-##############################################################################
-
-n_source_samples = 100
-n_target_samples = 100
-theta = 2 * np.pi / 20
-noise_level = 0.1
-
-Xs, ys = ot.datasets.get_data_classif(
-    'gaussrot', n_source_samples, nz=noise_level)
-Xs_new, _ = ot.datasets.get_data_classif(
-    'gaussrot', n_source_samples, nz=noise_level)
-Xt, yt = ot.datasets.get_data_classif(
-    'gaussrot', n_target_samples, theta=theta, nz=noise_level)
-
-# one of the target mode changes its variance (no linear mapping)
-Xt[yt == 2] *= 3
-Xt = Xt + 4
-
-
-##############################################################################
-# Instantiate the different transport algorithms and fit them
-##############################################################################
-
-# MappingTransport with linear kernel
-ot_mapping_linear = ot.da.MappingTransport(
-    kernel="linear", mu=1e0, eta=1e-8, bias=True,
-    max_iter=20, verbose=True)
-
-ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
-# for original source samples, transform applies barycentric mapping
-transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)
-
-# for out of source samples, transform applies the linear mapping
-transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)
-
-
-# MappingTransport with gaussian kernel
-ot_mapping_gaussian = ot.da.MappingTransport(
-    kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1,
-    max_iter=10, verbose=True)
-ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
-# for original source samples, transform applies barycentric mapping
-transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)
-
-# for out of source samples, transform applies the gaussian mapping
-transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)
-
-
-##############################################################################
-# plot data
-##############################################################################
-
-pl.figure(1, (10, 5))
-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')
-
-
-##############################################################################
-# plot transported 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(transp_Xs_linear[:, 0], transp_Xs_linear[:, 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(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 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(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 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(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,
-           marker='+', label='Learned mapping')
-pl.title("Estim. mapping (kernel)")
-pl.tight_layout()
-
-pl.show()
diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py
deleted file mode 100644
index a628b05..0000000
--- a/examples/da/plot_otda_mapping_colors_images.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-====================================================================================
-OT for domain adaptation with image color adaptation [6] with mapping estimation [8]
-====================================================================================
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
-    discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),
-    1853-1882.
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
-    discrete optimal transport", Neural Information Processing Systems (NIPS),
-    2016.
-
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-#          Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-from scipy import ndimage
-import matplotlib.pylab as pl
-import ot
-
-r = np.random.RandomState(42)
-
-
-def im2mat(I):
-    """Converts and image to matrix (one pixel per line)"""
-    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
-def mat2im(X, shape):
-    """Converts back a matrix to an image"""
-    return X.reshape(shape)
-
-
-def minmax(I):
-    return np.clip(I, 0, 1)
-
-
-##############################################################################
-# Generate data
-##############################################################################
-
-# Loading images
-I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-
-X1 = im2mat(I1)
-X2 = im2mat(I2)
-
-# training samples
-nb = 1000
-idx1 = r.randint(X1.shape[0], size=(nb,))
-idx2 = r.randint(X2.shape[0], size=(nb,))
-
-Xs = X1[idx1, :]
-Xt = X2[idx2, :]
-
-
-##############################################################################
-# Domain adaptation for pixel distribution transfer
-##############################################################################
-
-# EMDTransport
-ot_emd = ot.da.EMDTransport()
-ot_emd.fit(Xs=Xs, Xt=Xt)
-transp_Xs_emd = ot_emd.transform(Xs=X1)
-Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))
-
-# SinkhornTransport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)
-Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
-
-ot_mapping_linear = ot.da.MappingTransport(
-    mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)
-ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
-X1tl = ot_mapping_linear.transform(Xs=X1)
-Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))
-
-ot_mapping_gaussian = ot.da.MappingTransport(
-    mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)
-ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
-X1tn = ot_mapping_gaussian.transform(Xs=X1)  # use the estimated mapping
-Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))
-
-
-##############################################################################
-# plot original images
-##############################################################################
-
-pl.figure(1, figsize=(6.4, 3))
-pl.subplot(1, 2, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Image 2')
-pl.tight_layout()
-
-
-##############################################################################
-# plot pixel values distribution
-##############################################################################
-
-pl.figure(2, figsize=(6.4, 5))
-
-pl.subplot(1, 2, 1)
-pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 2')
-pl.tight_layout()
-
-
-##############################################################################
-# plot transformed images
-##############################################################################
-
-pl.figure(2, figsize=(10, 5))
-
-pl.subplot(2, 3, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Im. 1')
-
-pl.subplot(2, 3, 4)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Im. 2')
-
-pl.subplot(2, 3, 2)
-pl.imshow(Image_emd)
-pl.axis('off')
-pl.title('EmdTransport')
-
-pl.subplot(2, 3, 5)
-pl.imshow(Image_sinkhorn)
-pl.axis('off')
-pl.title('SinkhornTransport')
-
-pl.subplot(2, 3, 3)
-pl.imshow(Image_mapping_linear)
-pl.axis('off')
-pl.title('MappingTransport (linear)')
-
-pl.subplot(2, 3, 6)
-pl.imshow(Image_mapping_gaussian)
-pl.axis('off')
-pl.title('MappingTransport (gaussian)')
-pl.tight_layout()
-
-pl.show()
diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py
index 023e645..2a42dc0 100644
--- a/examples/plot_OT_2D_samples.py
+++ b/examples/plot_OT_2D_samples.py
@@ -65,7 +65,7 @@ pl.title('OT matrix with samples')
 #%% sinkhorn
 
 # reg term
-lambd = 5e-4
+lambd = 1e-3
 
 Gs = ot.sinkhorn(a, b, M, lambd)