remove W605 errors

1 files changed, 10 insertions, 10 deletions

diff --git a/ot/stochastic.py b/ot/stochastic.py
index ec53015..1376884 100644
--- a/ot/stochastic.py
+++ b/ot/stochastic.py
@@ -418,8 +418,8 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None,
         return None
 
     opt_alpha = c_transform_entropic(b, M, reg, opt_beta)
-    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) *
-          a[:, None] * b[None, :])
+    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg)
+          * a[:, None] * b[None, :])
 
     if log:
         log = {}
@@ -520,15 +520,15 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha,
                       arXiv preprint arxiv:1711.02283.
     '''
 
-    G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] -
-                   M[batch_alpha, :][:, batch_beta]) / reg) *
+    G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta]
+                   - M[batch_alpha, :][:, batch_beta]) / reg) *
            a[batch_alpha, None] * b[None, batch_beta])
     grad_beta = np.zeros(np.shape(M)[1])
     grad_alpha = np.zeros(np.shape(M)[0])
-    grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0] +
-                             G.sum(0))
-    grad_alpha[batch_alpha] = (a[batch_alpha] * len(batch_beta) /
-                               np.shape(M)[1] + G.sum(1))
+    grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0]
+                             + G.sum(0))
+    grad_alpha[batch_alpha] = (a[batch_alpha] * len(batch_beta)
+                               / np.shape(M)[1] + G.sum(1))
 
     return grad_alpha, grad_beta
 
@@ -702,8 +702,8 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1,
 
     opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size,
                                                       numItermax, lr)
-    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) *
-          a[:, None] * b[None, :])
+    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg)
+          * a[:, None] * b[None, :])
     if log:
         log = {}
         log['alpha'] = opt_alpha