new_contents: # -*- coding: utf-8 -*-
  """
  demo of Optimal transport for domain adaptation
  """
  
  import numpy as np
  import matplotlib.pylab as pl
  import ot
  
  
  
  #%% parameters
  
  n=150 # nb bins
  
  xs,ys=ot.datasets.get_data_classif('3gauss',n)
  xt,yt=ot.datasets.get_data_classif('3gauss2',n)
  
  a,b = ot.unif(n),ot.unif(n)
  # loss matrix
  M=ot.dist(xs,xt)
  #M/=M.max()
  
  #%% plot samples
  
  pl.figure(1)
  
  pl.subplot(2,2,1)
  pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
  pl.legend(loc=0)
  pl.title('Source  distributions')
  
  pl.subplot(2,2,2)
  pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')
  pl.legend(loc=0)
  pl.title('target  distributions')
  
  
  #%% OT estimation
  
  # EMD
  
  
  da_emd=ot.da.OTDA()
  da_emd.fit(xs,xt)
  
  # interpolate samples
  xst0=da_emd.interp()
  
  
  # sinkhorn
  lambd=1e-1
  da_entrop=ot.da.OTDA_sinkhorn()
  da_entrop.fit(xs,xt,reg=lambd)
  xsts=da_entrop.interp()
  
  # Group lasso regularization
  reg=1e-1
  eta=1e0
  da_lpl1=ot.da.OTDA_lpl1()
  da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta)
  xstg=da_lpl1.interp()
  
  #%% plot interpolated source samples
  pl.figure(4)
  
  param_img={'interpolation':'nearest','cmap':'jet'}
  
  pl.subplot(2,3,1)
  pl.imshow(da_emd.G,**param_img)
  pl.title('OT matrix')
  
  
  pl.subplot(2,3,2)
  pl.imshow(da_entrop.G,**param_img)
  pl.title('OT matrix sinkhorn')
  
  pl.subplot(2,3,3)
  pl.imshow(da_lpl1.G,**param_img)
  pl.title('OT matrix Group Lasso')
  
  pl.subplot(2,3,4)
  pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
  pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)
  pl.title('Interp samples')
  pl.legend(loc=0)
  
  pl.subplot(2,3,5)
  pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
  pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)
  pl.title('Interp samples Sinkhorn')
  
  pl.subplot(2,3,6)
  pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
  pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)
  pl.title('Interp samples Group Lasso')
subject: add domain adaptation demo