Corrections on Gromov

Author: Nicolas Courty

data/rond.png data/cross.pngdata/rond.png renamed to data/cross.png

@@ -22,8 +22,9 @@
 """
 Sample two Gaussian distributions (2D and 3D)
 =============================================
-The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space.
-For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces.
+The Gromov-Wasserstein distance allows to compute distances with samples that 
+do not belong to the same metric space. For demonstration purpose, we sample 
+two Gaussian distributions in 2- and 3-dimensional spaces.
 """
 
 n_samples = 30  # nb samples

data/carre.png data/square.pngdata/carre.png renamed to data/square.png

@@ -48,13 +48,10 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9):
     eps : float
         relative tolerance w.r.t stress to declare converge
 
-
     Returns
     -------
     npos : ndarray, shape (R, dim)
            Embedded coordinates of the interpolated point cloud (defined with one isometry)
-
-
     """
 
     rng = np.random.RandomState(seed=3)
@@ -91,12 +88,12 @@ def im2mat(I):
     return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
 
 
-square = spi.imread('../data/carre.png').astype(np.float64) / 256
-circle = spi.imread('../data/rond.png').astype(np.float64) / 256
-triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256
-arrow = spi.imread('../data/coeur.png').astype(np.float64) / 256
+square = spi.imread('../data/square.png').astype(np.float64)[:,:,2] / 256
+cross = spi.imread('../data/cross.png').astype(np.float64)[:,:,2] / 256
+triangle = spi.imread('../data/triangle.png').astype(np.float64)[:,:,2] / 256
+star = spi.imread('../data/star.png').astype(np.float64)[:,:,2] / 256
 
-shapes = [square, circle, triangle, arrow]
+shapes = [square, cross, triangle, star]
 
 S = 4
 xs = [[] for i in range(S)]

data/coeur.png data/star.pngdata/coeur.png renamed to data/star.png

@@ -17,8 +17,7 @@ def test_gromov():
 
     xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
 
-    xt = xs[::-1]
-    xt = np.array(xt)
+    xt = xs[::-1].copy()
 
     p = ot.unif(n_samples)
     q = ot.unif(n_samples)