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diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py
deleted file mode 100644
index f07dc6c..0000000
--- a/examples/plot_OTDA_mapping_color_images.py
+++ /dev/null
@@ -1,158 +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.
-
-"""
-
-import numpy as np
-import scipy.ndimage as spi
-import matplotlib.pylab as pl
-import ot
-
-
-#%% Loading images
-
-I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256
-I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256
-
-#%% Plot images
-
-pl.figure(1)
-
-pl.subplot(1,2,1)
-pl.imshow(I1)
-pl.title('Image 1')
-
-pl.subplot(1,2,2)
-pl.imshow(I2)
-pl.title('Image 2')
-
-pl.show()
-
-#%% Image conversion and dataset generation
-
-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)
-
-X1=im2mat(I1)
-X2=im2mat(I2)
-
-# training samples
-nb=1000
-idx1=np.random.randint(X1.shape[0],size=(nb,))
-idx2=np.random.randint(X2.shape[0],size=(nb,))
-
-xs=X1[idx1,:]
-xt=X2[idx2,:]
-
-#%% Plot image distributions
-
-
-pl.figure(2,(10,5))
-
-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.imshow(I2)
-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.show()
-
-
-
-#%% domain adaptation between images
-def minmax(I):
-    return np.minimum(np.maximum(I,0),1)
-# LP problem
-da_emd=ot.da.OTDA()     # init class
-da_emd.fit(xs,xt)       # fit distributions
-
-X1t=da_emd.predict(X1)  # out of sample
-I1t=minmax(mat2im(X1t,I1.shape))
-
-# sinkhorn regularization
-lambd=1e-1
-da_entrop=ot.da.OTDA_sinkhorn()
-da_entrop.fit(xs,xt,reg=lambd)
-
-X1te=da_entrop.predict(X1)
-I1te=minmax(mat2im(X1te,I1.shape))
-
-# 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)
-
-X1tl=ot_mapping.predict(X1) # use the estimated mapping
-I1tl=minmax(mat2im(X1tl,I1.shape))
-
-# nonlinear mapping estimation
-eta=1e-2   # quadratic regularization for regression
-mu=1e0     # weight of the OT linear term
-bias=False  # 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)
-
-X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping
-I1tn=minmax(mat2im(X1tn,I1.shape))
-#%% plot images
-
-
-pl.figure(2,(10,8))
-
-pl.subplot(2,3,1)
-
-pl.imshow(I1)
-pl.title('Im. 1')
-
-pl.subplot(2,3,2)
-
-pl.imshow(I2)
-pl.title('Im. 2')
-
-
-pl.subplot(2,3,3)
-pl.imshow(I1t)
-pl.title('Im. 1 Interp LP')
-
-pl.subplot(2,3,4)
-pl.imshow(I1te)
-pl.title('Im. 1 Interp Entrop')
-
-
-pl.subplot(2,3,5)
-pl.imshow(I1tl)
-pl.title('Im. 1 Linear mapping')
-
-pl.subplot(2,3,6)
-pl.imshow(I1tn)
-pl.title('Im. 1 nonlinear mapping')
-
-pl.show()