pep8

1 files changed, 8 insertions, 8 deletions

diff --git a/ot/gpu/da.py b/ot/gpu/da.py
index 8bcc2aa..8c63870 100644
--- a/ot/gpu/da.py
+++ b/ot/gpu/da.py
@@ -10,15 +10,16 @@ Domain adaptation with optimal transport with GPU implementation
 #
 # License: MIT License
 
-import cupy as np # np used for matrix computation
-import cupy as cp # cp used for cupy specific operations
+import cupy as np  # np used for matrix computation
+import cupy as cp  # cp used for cupy specific operations
 import numpy as npp
 from . import utils
 from .bregman import sinkhorn
 
+
 def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
                      numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
-                     log=False,to_numpy=True):
+                     log=False, to_numpy=True):
     """
     Solve the entropic regularization optimal transport problem with nonconvex
     group lasso regularization
@@ -101,15 +102,14 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
     ot.optim.cg : General regularized OT
 
     """
-    
+
     a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M)
-    
-    
+
     p = 0.5
     epsilon = 1e-3
 
     indices_labels = []
-    labels_a2=cp.asnumpy(labels_a)
+    labels_a2 = cp.asnumpy(labels_a)
     classes = npp.unique(labels_a2)
     for c in classes:
         idxc, = utils.to_gpu(npp.where(labels_a2 == c))
@@ -120,7 +120,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
     for cpt in range(numItermax):
         Mreg = M + eta * W
         transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
-                          stopThr=stopInnerThr,to_numpy=False)
+                          stopThr=stopInnerThr, to_numpy=False)
         # the transport has been computed. Check if classes are really
         # separated
         W = np.ones(M.shape)