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Author: agramfort

examples/plot_WDA.py

@@ -16,81 +16,83 @@
 
 #%% parameters
 
-n=1000 # nb samples in source and target datasets
-nz=0.2
+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
+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)
+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
+nbnoise = 8
 
-xs=np.hstack((xs,np.random.randn(n,nbnoise)))
-xt=np.hstack((xt,np.random.randn(n,nbnoise)))
+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, figsize=(6.4, 3.5))
 
-pl.subplot(1,2,1)
-pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')
+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.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()
+pl.tight_layout()
 
 #%% Compute FDA
-p=2
+p = 2
 
-Pfda,projfda = fda(xs,ys,p)
+Pfda, projfda = fda(xs, ys, p)
 
 #%% Compute WDA
-p=2
-reg=1e0
-k=10
-maxiter=100
+p = 2
+reg = 1e0
+k = 10
+maxiter = 100
 
-Pwda,projwda = 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)
+xsp = projfda(xs)
+xtp = projfda(xt)
 
-xspw=projwda(xs)
-xtpw=projwda(xt)
+xspw = projwda(xs)
+xtpw = projwda(xt)
 
-pl.figure(1,(10,10))
+pl.figure(2)
 
-pl.subplot(2,2,1)
-pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')
+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.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.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.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')
+pl.tight_layout()
+
+pl.show()

requirements.txt

@@ -3,3 +3,5 @@ scipy
 cython
 matplotlib
 sphinx-gallery
+autograd
+pymanopt