update wda example

Author: rflamary

examples/plot_WDA.py

@@ -18,51 +18,79 @@
 
 n=1000 # nb samples in source and target datasets
 nz=0.2
-xs,ys=ot.datasets.get_data_classif('3gauss',n,nz)
-xt,yt=ot.datasets.get_data_classif('3gauss',n,nz)
+
+# 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.figure(1)
-
-
+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()
 
-#%% Comlpute FDA
+#%% Compute FDA
 p=2
 
 Pfda,projfda = fda(xs,ys,p)
 
 #%% Compute WDA
 p=2
-reg=1
+reg=1e-1
 k=10
 maxiter=100
 
-P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)
+Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter)
 
 #%% plot samples
 
 xsp=projfda(xs)
 xtp=projfda(xt)
 
-pl.figure(1,(10,5))
+xspw=projwda(xs)
+xtpw=projwda(xt)
 
-pl.subplot(1,2,1)
+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')
+pl.title('Projected training samples FDA')
 
 
-pl.subplot(1,2,2)
+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')
+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')