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# -*- coding: utf-8 -*-
"""
=================================
Wasserstein Discriminant Analysis
=================================
@author: rflamary
"""
import numpy as np
import matplotlib.pylab as pl
import ot
from ot.datasets import get_1D_gauss as gauss
from ot.dr import wda, fda
#%% parameters
n=1000 # nb samples in source and target datasets
nz=0.2
# generate circle dataset
t=np.random.rand(n)*2*np.pi
ys=np.floor((np.arange(n)*1.0/n*3))+1
xs=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)
xs=xs*ys.reshape(-1,1)+nz*np.random.randn(n,2)
t=np.random.rand(n)*2*np.pi
yt=np.floor((np.arange(n)*1.0/n*3))+1
xt=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)
xt=xt*yt.reshape(-1,1)+nz*np.random.randn(n,2)
nbnoise=8
xs=np.hstack((xs,np.random.randn(n,nbnoise)))
xt=np.hstack((xt,np.random.randn(n,nbnoise)))
#%% plot samples
pl.figure(1,(10,5))
pl.subplot(1,2,1)
pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')
pl.legend(loc=0)
pl.title('Discriminant dimensions')
pl.subplot(1,2,2)
pl.scatter(xt[:,2],xt[:,3],c=ys,marker='+',label='Source samples')
pl.legend(loc=0)
pl.title('Other dimensions')
pl.show()
#%% Compute FDA
p=2
Pfda,projfda = fda(xs,ys,p)
#%% Compute WDA
p=2
reg=1e0
k=10
maxiter=100
Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter)
#%% plot samples
xsp=projfda(xs)
xtp=projfda(xt)
xspw=projwda(xs)
xtpw=projwda(xt)
pl.figure(1,(10,10))
pl.subplot(2,2,1)
pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')
pl.legend(loc=0)
pl.title('Projected training samples FDA')
pl.subplot(2,2,2)
pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')
pl.legend(loc=0)
pl.title('Projected test samples FDA')
pl.subplot(2,2,3)
pl.scatter(xspw[:,0],xspw[:,1],c=ys,marker='+',label='Projected samples')
pl.legend(loc=0)
pl.title('Projected training samples WDA')
pl.subplot(2,2,4)
pl.scatter(xtpw[:,0],xtpw[:,1],c=ys,marker='+',label='Projected samples')
pl.legend(loc=0)
pl.title('Projected test samples WDA')
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