docstrings and naming

1 files changed, 35 insertions, 0 deletions

diff --git a/ot/gromov.py b/ot/gromov.py
index ad85fcd..197e3ea 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -208,7 +208,11 @@ def update_kl_loss(p, lambdas, T, Cs):
     return(np.exp(np.divide(tmpsum, ppt)))

 

 

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

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

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
     """

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

 

@@ -248,7 +252,11 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1
     loss_fun :  loss function used for the solver either 'square_loss' or 'kl_loss'

     epsilon : float

         Regularization term >0

+<<<<<<< HEAD
     max_iter : int, optional

+=======
+    numItermax : int, optional

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
         Max number of iterations

     stopThr : float, optional

         Stop threshold on error (>0)

@@ -274,7 +282,11 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1
     cpt = 0

     err = 1

 

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

+=======
+    while (err > stopThr and cpt < numItermax):

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
 

         Tprev = T

 

@@ -307,7 +319,11 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1
         return T

 

 

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

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

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
     """

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

 

@@ -362,10 +378,17 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=
 

     if log:

         gw, logv = gromov_wasserstein(

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

     else:

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

                                 epsilon, max_iter, stopThr, verbose, log)

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

+    else:

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

+                                epsilon, numItermax, stopThr, verbose, log)

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
 

     if loss_fun == 'square_loss':

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

@@ -379,7 +402,11 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=
         return gw_dist

 

 

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

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

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
     """

     Returns the gromov-wasserstein barycenters of S measured similarity matrices

 

@@ -442,12 +469,20 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000,
 

     error = []

 

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

+=======
+    while(err > stopThr and cpt < numItermax):

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
 

         Cprev = C

 

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

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

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

+>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d
 

         if loss_fun == 'square_loss':

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