diff options
Diffstat (limited to 'examples/plot_OTDA_2D.py')
| -rw-r--r-- | examples/plot_OTDA_2D.py | 114 |
1 files changed, 58 insertions, 56 deletions
diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py index a1fb804..1bda59c 100644 --- a/examples/plot_OTDA_2D.py +++ b/examples/plot_OTDA_2D.py @@ -11,110 +11,112 @@ import matplotlib.pylab as pl import ot - #%% parameters -n=150 # nb bins +n = 150 # nb bins -xs,ys=ot.datasets.get_data_classif('3gauss',n) -xt,yt=ot.datasets.get_data_classif('3gauss2',n) +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) +a, b = ot.unif(n), ot.unif(n) # loss matrix -M=ot.dist(xs,xt) -#M/=M.max() +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.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.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') pl.figure(2) -pl.imshow(M,interpolation='nearest') +pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') #%% OT estimation # EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) # sinkhorn -lambd=1e-1 -Gs=ot.sinkhorn(a,b,M,lambd) +lambd = 1e-1 +Gs = ot.sinkhorn(a, b, M, lambd) # Group lasso regularization -reg=1e-1 -eta=1e0 -Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) +reg = 1e-1 +eta = 1e0 +Gg = ot.da.sinkhorn_lpl1_mm(a, ys.astype(np.int), b, M, reg, eta) #%% visu matrices pl.figure(3) -pl.subplot(2,3,1) -pl.imshow(G0,interpolation='nearest') +pl.subplot(2, 3, 1) +pl.imshow(G0, interpolation='nearest') pl.title('OT matrix ') -pl.subplot(2,3,2) -pl.imshow(Gs,interpolation='nearest') +pl.subplot(2, 3, 2) +pl.imshow(Gs, interpolation='nearest') pl.title('OT matrix Sinkhorn') -pl.subplot(2,3,3) -pl.imshow(Gg,interpolation='nearest') +pl.subplot(2, 3, 3) +pl.imshow(Gg, interpolation='nearest') pl.title('OT matrix Group lasso') -pl.subplot(2,3,4) -ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.subplot(2,3,5) -ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(xs, xt, Gs, c=[.5, .5, 1]) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.subplot(2,3,6) -ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(xs, xt, Gg, c=[.5, .5, 1]) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') +pl.tight_layout() #%% sample interpolation -xst0=n*G0.dot(xt) -xsts=n*Gs.dot(xt) -xstg=n*Gg.dot(xt) - -pl.figure(4) -pl.subplot(2,3,1) - +xst0 = n * G0.dot(xt) +xsts = n * Gs.dot(xt) +xstg = n * Gg.dot(xt) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.figure(4, figsize=(8, 3)) +pl.subplot(1, 3, 1) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, + marker='+', label='Transp samples', s=30) pl.title('Interp samples') pl.legend(loc=0) -pl.subplot(2,3,2) - - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.subplot(1, 3, 2) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(xsts[:, 0], xsts[:, 1], c=ys, + marker='+', label='Transp samples', s=30) pl.title('Interp samples Sinkhorn') -pl.subplot(2,3,3) - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Grouplasso')
\ No newline at end of file +pl.subplot(1, 3, 3) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(xstg[:, 0], xstg[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Interp samples Grouplasso') +pl.tight_layout() +pl.show() |