diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/plot_gromov.py | 29 | ||||
| -rwxr-xr-x | examples/plot_gromov_barycenter.py | 8 |
2 files changed, 20 insertions, 17 deletions
diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index d42c21a..5f2d826 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -81,23 +81,26 @@ pl.show() #%%
p = ot.unif(n_samples)
q = ot.unif(n_samples)
-ot.tic()
-gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,verbose=True)
-ot.toc()
-ot.tic()
-gw2,log2= ot.gromov.gromov_wasserstein0(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True)
-ot.toc()
-gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)
+gw0,log0 = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True,log=True)
-ot.tic()
-gw0,log0=ot.gromov.gw_lp(C1, C2, p, q, 'square_loss',log=True,verbose=True)
-ot.toc()
+gw,log= ot.gromov.entropic_gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True)
-print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))
+print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
+print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))
-pl.figure()
-pl.imshow(gw2, cmap='jet')
+
+pl.figure(1,(10,5))
+
+pl.subplot(1,2,1)
+pl.imshow(gw0, cmap='jet')
pl.colorbar()
+pl.title('Gromov Wasserstein')
+
+pl.subplot(1,2,2)
+pl.imshow(gw0, cmap='jet')
+pl.colorbar()
+pl.title('Entropic Gromov Wasserstein')
+
pl.show()
diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 180b0cf..fde822b 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -132,28 +132,28 @@ Ct01 = [0 for i in range(2)] for i in range(2):
Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],
[ps[0], ps[1]
- ], p, lambdast[i], 'square_loss', 5e-4,
+ ], p, lambdast[i], 'square_loss', #5e-4,
max_iter=100, tol=1e-3)
Ct02 = [0 for i in range(2)]
for i in range(2):
Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],
[ps[0], ps[2]
- ], p, lambdast[i], 'square_loss', 5e-4,
+ ], p, lambdast[i], 'square_loss',# 5e-4,
max_iter=100, tol=1e-3)
Ct13 = [0 for i in range(2)]
for i in range(2):
Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],
[ps[1], ps[3]
- ], p, lambdast[i], 'square_loss', 5e-4,
+ ], p, lambdast[i], 'square_loss',# 5e-4,
max_iter=100, tol=1e-3)
Ct23 = [0 for i in range(2)]
for i in range(2):
Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],
[ps[2], ps[3]
- ], p, lambdast[i], 'square_loss', 5e-4,
+ ], p, lambdast[i], 'square_loss', #5e-4,
max_iter=100, tol=1e-3)
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