# -*- 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')