pep8

Author: rflamary

ot/__init__.py

@@ -28,6 +28,6 @@
 
 __version__ = "0.3.1"
 
-__all__ = ["emd", "emd2", "sinkhorn","sinkhorn2", "utils", 'datasets',
+__all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets',
            'bregman', 'lp', 'plot', 'tic', 'toc', 'toq',
            'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim']

ot/bregman.py

@@ -108,7 +108,8 @@ def sink():
                                        stopThr=stopThr, verbose=verbose, log=log, **kwargs)
     elif method.lower() == 'sinkhorn_epsilon_scaling':
         def sink():
-            return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax,
+            return sinkhorn_epsilon_scaling(
+                a, b, M, reg, numItermax=numItermax,
                                             stopThr=stopThr, verbose=verbose, log=log, **kwargs)
     else:
         print('Warning : unknown method using classic Sinkhorn Knopp')
@@ -216,7 +217,8 @@ def sink():
                                        stopThr=stopThr, verbose=verbose, log=log, **kwargs)
     elif method.lower() == 'sinkhorn_epsilon_scaling':
         def sink():
-            return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax,
+            return sinkhorn_epsilon_scaling(
+                a, b, M, reg, numItermax=numItermax,
                                             stopThr=stopThr, verbose=verbose, log=log, **kwargs)
     else:
         print('Warning : unknown method using classic Sinkhorn Knopp')
@@ -593,7 +595,7 @@ def get_Gamma(alpha, beta, u, v):
 
         cpt = cpt + 1
 
-    #print('err=',err,' cpt=',cpt)
+    # print('err=',err,' cpt=',cpt)
     if log:
         log['logu'] = alpha / reg + np.log(u)
         log['logv'] = beta / reg + np.log(v)
@@ -778,7 +780,7 @@ def get_reg(n):  # exponential decreasing
             loop = False
 
         cpt = cpt + 1
-    #print('err=',err,' cpt=',cpt)
+    # print('err=',err,' cpt=',cpt)
     if log:
         log['alpha'] = alpha
         log['beta'] = beta
@@ -965,16 +967,16 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verb
 
     """
 
-    #M = M/np.median(M)
+    # M = M/np.median(M)
     K = np.exp(-M / reg)
 
-    #M0 = M0/np.median(M0)
+    # M0 = M0/np.median(M0)
     K0 = np.exp(-M0 / reg0)
     old = h0
 
     err = 1
     cpt = 0
-    #log = {'niter':0, 'all_err':[]}
+    # log = {'niter':0, 'all_err':[]}
     if log:
         log = {'err': []}
 

ot/da.py

@@ -478,7 +478,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm
         Kp[:ns, :ns] = K
 
         # ls regu
-        #K0 = K1.T.dot(K1)+eta*I
+        # K0 = K1.T.dot(K1)+eta*I
         # Kreg=I
 
         # RKHS regul
@@ -490,7 +490,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm
         I = np.eye(ns)
 
         # ls regul
-        #K0 = K1.T.dot(K1)+eta*I
+        # K0 = K1.T.dot(K1)+eta*I
         # Kreg=I
 
         # proper kernel ridge

ot/optim.py

@@ -304,7 +304,7 @@ def cost(G):
         Mi = M + reg2 * df(G)
 
         # solve linear program with Sinkhorn
-        #Gc = sinkhorn_stabilized(a,b, Mi, reg1, numItermax = numInnerItermax)
+        # Gc = sinkhorn_stabilized(a,b, Mi, reg1, numItermax = numInnerItermax)
         Gc = sinkhorn(a, b, Mi, reg1, numItermax=numInnerItermax)
 
         deltaG = Gc - G