gromov:flake8 and other

1 files changed, 27 insertions, 36 deletions

diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py
index 11e5336..a33fde1 100644
--- a/examples/plot_gromov.py
+++ b/examples/plot_gromov.py
@@ -3,11 +3,8 @@
 ====================

 Gromov-Wasserstein example

 ====================

-

-This example is designed to show how to use the Gromov-Wassertsein distance 

-computation in POT. 

-

-

+This example is designed to show how to use the Gromov-Wassertsein distance

+computation in POT.

 """

 

 # Author: Erwan Vautier <erwan.vautier@gmail.com>

@@ -20,43 +17,38 @@ import numpy as np
 

 import ot

 import matplotlib.pylab as pl

-from mpl_toolkits.mplot3d import Axes3D

-

 

 

 """

 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=30 # nb samples

 

-mu_s=np.array([0,0])

-cov_s=np.array([[1,0],[0,1]])

+n = 30  # nb samples

 

-mu_t=np.array([4,4,4])

-cov_t=np.array([[1,0,0],[0,1,0],[0,0,1]])

+mu_s = np.array([0, 0])

+cov_s = np.array([[1, 0], [0, 1]])

 

+mu_t = np.array([4, 4, 4])

+cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])

 

 

-xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)

-P=sp.linalg.sqrtm(cov_t)

-xt= np.random.randn(n,3).dot(P)+mu_t

-

+xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)

+P = sp.linalg.sqrtm(cov_t)

+xt = np.random.randn(n, 3).dot(P) + mu_t

 

 

 """

 Plotting the distributions

 ====================

 """

-fig=pl.figure()

-ax1=fig.add_subplot(121)

-ax1.plot(xs[:,0],xs[:,1],'+b',label='Source samples')

-ax2=fig.add_subplot(122,projection='3d')

-ax2.scatter(xt[:,0],xt[:,1],xt[:,2],color='r')

+fig = pl.figure()

+ax1 = fig.add_subplot(121)

+ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')

+ax2 = fig.add_subplot(122, projection='3d')

+ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')

 pl.show()

 

 

@@ -65,11 +57,11 @@ Compute distance kernels, normalize them and then display
 ====================

 """

 

-C1=sp.spatial.distance.cdist(xs,xs)

-C2=sp.spatial.distance.cdist(xt,xt)

+C1 = sp.spatial.distance.cdist(xs, xs)

+C2 = sp.spatial.distance.cdist(xt, xt)

 

-C1/=C1.max()

-C2/=C2.max()

+C1 /= C1.max()

+C2 /= C2.max()

 

 pl.figure()

 pl.subplot(121)

@@ -83,16 +75,15 @@ Compute Gromov-Wasserstein plans and distance
 ====================

 """

 

-p=ot.unif(n)

-q=ot.unif(n)

+p = ot.unif(n)

+q = ot.unif(n)

 

-gw=ot.gromov_wasserstein(C1,C2,p,q,'square_loss',epsilon=5e-4)

-gw_dist=ot.gromov_wasserstein2(C1,C2,p,q,'square_loss',epsilon=5e-4)

+gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)

+gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)

 

-print('Gromov-Wasserstein distances between the distribution: '+str(gw_dist))

+print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))

 

 pl.figure()

-pl.imshow(gw,cmap='jet')

+pl.imshow(gw, cmap='jet')

 pl.colorbar()

 pl.show()

-