docstrings and naming

4 files changed, 29 insertions, 29 deletions

diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py
index 9bbdbde..92312ae 100644
--- a/examples/plot_gromov.py
+++ b/examples/plot_gromov.py
@@ -26,7 +26,7 @@ The Gromov-Wasserstein distance allows to compute distances with samples that do
 For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces.

 """

 

-n = 30  # nb samples

+n_samples = 30  # nb samples

 

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

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

@@ -35,9 +35,9 @@ 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)

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

 P = sp.linalg.sqrtm(cov_t)

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

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

 

 

 """

@@ -75,8 +75,8 @@ Compute Gromov-Wasserstein plans and distance
 =============================================

 """

 

-p = ot.unif(n)

-q = ot.unif(n)

+p = ot.unif(n_samples)

+q = ot.unif(n_samples)

 

 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)

diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py
index da52768..f0657e1 100755
--- a/examples/plot_gromov_barycenter.py
+++ b/examples/plot_gromov_barycenter.py
@@ -91,12 +91,12 @@ def im2mat(I):
     return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))

 

 

-carre = spi.imread('../data/carre.png').astype(np.float64) / 256

-rond = spi.imread('../data/rond.png').astype(np.float64) / 256

+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

-fleche = spi.imread('../data/coeur.png').astype(np.float64) / 256

+arrow = spi.imread('../data/coeur.png').astype(np.float64) / 256

 

-shapes = [carre, rond, triangle, fleche]

+shapes = [square, circle, triangle, arrow]

 

 S = 4

 xs = [[] for i in range(S)]

@@ -118,36 +118,36 @@ Barycenter computation
 The four distributions are constructed from 4 simple images

 """

 ns = [len(xs[s]) for s in range(S)]

-N = 30

+n_samples = 30

 

 """Compute all distances matrices for the four shapes"""

 Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]

 Cs = [cs / cs.max() for cs in Cs]

 

 ps = [ot.unif(ns[s]) for s in range(S)]

-p = ot.unif(N)

+p = ot.unif(n_samples)

 

 

 lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]

 

 Ct01 = [0 for i in range(2)]

 for i in range(2):

-    Ct01[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[1]], [

+    Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [

                                            ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)

 

 Ct02 = [0 for i in range(2)]

 for i in range(2):

-    Ct02[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[2]], [

+    Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [

                                            ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)

 

 Ct13 = [0 for i in range(2)]

 for i in range(2):

-    Ct13[i] = ot.gromov.gromov_barycenters(N, [Cs[1], Cs[3]], [

+    Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [

                                            ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)

 

 Ct23 = [0 for i in range(2)]

 for i in range(2):

-    Ct23[i] = ot.gromov.gromov_barycenters(N, [Cs[2], Cs[3]], [

+    Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [

                                            ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)

 

 """

diff --git a/ot/gromov.py b/ot/gromov.py
index 421ed3f..ad85fcd 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -208,7 +208,7 @@ def update_kl_loss(p, lambdas, T, Cs):
     return(np.exp(np.divide(tmpsum, ppt)))

 

 

-def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):

+def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):

     """

     Returns the gromov-wasserstein coupling between the two measured similarity matrices

 

@@ -248,7 +248,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr
     loss_fun :  loss function used for the solver either 'square_loss' or 'kl_loss'

     epsilon : float

         Regularization term >0

-    numItermax : int, optional

+    max_iter : int, optional

         Max number of iterations

     stopThr : float, optional

         Stop threshold on error (>0)

@@ -274,7 +274,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr
     cpt = 0

     err = 1

 

-    while (err > stopThr and cpt < numItermax):

+    while (err > stopThr and cpt < max_iter):

 

         Tprev = T

 

@@ -307,7 +307,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr
         return T

 

 

-def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):

+def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):

     """

     Returns the gromov-wasserstein discrepancy between the two measured similarity matrices

 

@@ -362,10 +362,10 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh
 

     if log:

         gw, logv = gromov_wasserstein(

-            C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log)

+            C1, C2, p, q, loss_fun, epsilon, max_iter, stopThr, verbose, log)

     else:

         gw = gromov_wasserstein(C1, C2, p, q, loss_fun,

-                                epsilon, numItermax, stopThr, verbose, log)

+                                epsilon, max_iter, stopThr, verbose, log)

 

     if loss_fun == 'square_loss':

         gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw))

@@ -379,7 +379,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh
         return gw_dist

 

 

-def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):

+def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):

     """

     Returns the gromov-wasserstein barycenters of S measured similarity matrices

 

@@ -442,12 +442,12 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000
 

     error = []

 

-    while(err > stopThr and cpt < numItermax):

+    while(err > stopThr and cpt < max_iter):

 

         Cprev = C

 

         T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon,

-                                numItermax, 1e-5, verbose, log) for s in range(S)]

+                                max_iter, 1e-5, verbose, log) for s in range(S)]

 

         if loss_fun == 'square_loss':

             C = update_square_loss(p, lambdas, T, Cs)

diff --git a/test/test_gromov.py b/test/test_gromov.py
index 75eeaab..c26d898 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -10,18 +10,18 @@ import ot
 

 

 def test_gromov():

-    n = 50  # nb samples

+    n_samples = 50  # nb samples

 

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

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

 

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

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

 

-    xt = [xs[n - (i + 1)] for i in range(n)]

+    xt = [xs[n_samples - (i + 1)] for i in range(n_samples)]

     xt = np.array(xt)

 

-    p = ot.unif(n)

-    q = ot.unif(n)

+    p = ot.unif(n_samples)

+    q = ot.unif(n_samples)

 

     C1 = ot.dist(xs, xs)

     C2 = ot.dist(xt, xt)