fix pep8

Author: Kilian Fatras

ot/bregman.py

@@ -1375,17 +1375,18 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI
     '''
 
     if a is None:
-        a = ot.unif(np.shape(X_s)[0])
+        a = utils.unif(np.shape(X_s)[0])
     if b is None:
-        b = ot.unif(np.shape(X_t)[0])
+        b = utils.unif(np.shape(X_t)[0])
+
     M = ot.dist(X_s, X_t, metric=metric)
-    if log == False:
-        pi = ot.sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=False, **kwargs)
-        return pi
 
-    if log == True:
-        pi, log = ot.sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=True, **kwargs)
+    if log:
+        pi, log = sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=True, **kwargs)
         return pi, log
+    else:
+        pi = sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=False, **kwargs)
+        return pi
 
 
 def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
@@ -1464,18 +1465,18 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num
     '''
 
     if a is None:
-        a = ot.unif(np.shape(X_s)[0])
+        a = utils.unif(np.shape(X_s)[0])
     if b is None:
-        b = ot.unif(np.shape(X_t)[0])
+        b = utils.unif(np.shape(X_t)[0])
 
     M = ot.dist(X_s, X_t, metric=metric)
-    if log == False:
-        sinkhorn_loss = ot.sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
-        return sinkhorn_loss
 
-    if log == True:
-        sinkhorn_loss, log = ot.sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
+    if log:
+        sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
         return sinkhorn_loss, log
+    else:
+        sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
+        return sinkhorn_loss
 
 
 def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):

test/test_bregman.py

@@ -195,13 +195,11 @@ def test_empirical_sinkhorn():
     n = 100
     a = ot.unif(n)
     b = ot.unif(n)
-    M = ot.dist(X_s, X_t)
-    M_e = ot.dist(X_s, X_t, metric='euclidean')
-
-    rng = np.random.RandomState(0)
 
     X_s = np.reshape(np.arange(n), (n, 1))
     X_t = np.reshape(np.arange(0, n), (n, 1))
+    M = ot.dist(X_s, X_t)
+    M_e = ot.dist(X_s, X_t, metric='euclidean')
 
     G_sqe = ot.bregman.empirical_sinkhorn(X_s, X_t, 1)
     sinkhorn_sqe = ot.sinkhorn(a, b, M, 1)