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diff --git a/docs/source/auto_examples/plot_OTDA_mapping.py b/docs/source/auto_examples/plot_OTDA_mapping.py
deleted file mode 100644
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--- a/docs/source/auto_examples/plot_OTDA_mapping.py
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@@ -1,110 +0,0 @@
-# -*- 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.
-"""
-
-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,(8,5))
-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')
-
-
-
-#%% 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,(10,7))
-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")
-
-
-
-#%% 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,(10,7))
-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)")