pep8

1 files changed, 32 insertions, 35 deletions

diff --git a/ot/smooth.py b/ot/smooth.py
index f8bb20a..f4f4306 100644
--- a/ot/smooth.py
+++ b/ot/smooth.py
@@ -1,4 +1,4 @@
-#Copyright (c) 2018, Mathieu Blondel 
+#Copyright (c) 2018, Mathieu Blondel
 #All rights reserved.
 #
 #Redistribution and use in source and binary forms, with or without
@@ -22,7 +22,7 @@
 #OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 #THE POSSIBILITY OF SUCH DAMAGE.
 
-# Author: Mathieu Blondel 
+# Author: Mathieu Blondel
 #         Remi Flamary <remi.flamary@unice.fr>
 
 """
@@ -39,7 +39,7 @@ from scipy.optimize import minimize
 
 def projection_simplex(V, z=1, axis=None):
     """ Projection of x onto the simplex, scaled by z
-    
+
         P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2
     z: float or array
         If array, len(z) must be compatible with V
@@ -65,19 +65,19 @@ def projection_simplex(V, z=1, axis=None):
     else:
         V = V.ravel().reshape(1, -1)
         return projection_simplex(V, z, axis=1).ravel()
-    
+
 
 class Regularization(object):
 
     def __init__(self, gamma=1.0):
         """
-        
+
         Parameters
         ----------
         gamma: float
             Regularization parameter.
             We recover unregularized OT when gamma -> 0.
-            
+
         """
         self.gamma = gamma
 
@@ -85,12 +85,12 @@ class Regularization(object):
         """
         Compute delta_Omega(X[:, j]) for each X[:, j].
         delta_Omega(x) = sup_{y >= 0} y^T x - Omega(y).
-        
+
         Parameters
         ----------
         X: array, shape = len(a) x len(b)
             Input array.
-            
+
         Returns
         -------
         v: array, len(b)
@@ -104,12 +104,12 @@ class Regularization(object):
         """
         Compute max_Omega_j(X[:, j]) for each X[:, j].
         max_Omega_j(x) = sup_{y >= 0, sum(y) = 1} y^T x - Omega(b[j] y) / b[j].
-        
+
         Parameters
         ----------
         X: array, shape = len(a) x len(b)
             Input array.
-            
+
         Returns
         -------
         v: array, len(b)
@@ -122,12 +122,12 @@ class Regularization(object):
     def Omega(T):
         """
         Compute regularization term.
-        
+
         Parameters
         ----------
         T: array, shape = len(a) x len(b)
             Input array.
-            
+
         Returns
         -------
         value: float
@@ -176,7 +176,7 @@ class SquaredL2(Regularization):
 def dual_obj_grad(alpha, beta, a, b, C, regul):
     """
     Compute objective value and gradients of dual objective.
-    
+
     Parameters
     ----------
     alpha: array, shape = len(a)
@@ -189,7 +189,7 @@ def dual_obj_grad(alpha, beta, a, b, C, regul):
         Ground cost matrix.
     regul: Regularization object
         Should implement a delta_Omega(X) method.
-        
+
     Returns
     -------
     obj: float
@@ -220,7 +220,7 @@ def dual_obj_grad(alpha, beta, a, b, C, regul):
 def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
     """
     Solve the "smoothed" dual objective.
-    
+
     Parameters
     ----------
     a: array, shape = len(a)
@@ -236,7 +236,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
         Tolerance parameter.
     max_iter: int
         Maximum number of iterations.
-        
+
     Returns
     -------
     alpha: array, shape = len(a)
@@ -275,7 +275,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
 def semi_dual_obj_grad(alpha, a, b, C, regul):
     """
     Compute objective value and gradient of semi-dual objective.
-    
+
     Parameters
     ----------
     alpha: array, shape = len(a)
@@ -287,7 +287,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul):
         Ground cost matrix.
     regul: Regularization object
         Should implement a max_Omega(X) method.
-        
+
     Returns
     -------
     obj: float
@@ -314,7 +314,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul):
 def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
     """
     Solve the "smoothed" semi-dual objective.
-    
+
     Parameters
     ----------
     a: array, shape = len(a)
@@ -330,7 +330,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
         Tolerance parameter.
     max_iter: int
         Maximum number of iterations.
-        
+
     Returns
     -------
     alpha: array, shape = len(a)
@@ -353,7 +353,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
 def get_plan_from_dual(alpha, beta, C, regul):
     """
     Retrieve optimal transportation plan from optimal dual potentials.
-    
+
     Parameters
     ----------
     alpha: array, shape = len(a)
@@ -363,7 +363,7 @@ def get_plan_from_dual(alpha, beta, C, regul):
         Ground cost matrix.
     regul: Regularization object
         Should implement a delta_Omega(X) method.
-        
+
     Returns
     -------
     T: array, shape = len(a) x len(b)
@@ -376,7 +376,7 @@ def get_plan_from_dual(alpha, beta, C, regul):
 def get_plan_from_semi_dual(alpha, b, C, regul):
     """
     Retrieve optimal transportation plan from optimal semi-dual potentials.
-    
+
     Parameters
     ----------
     alpha: array, shape = len(a)
@@ -387,7 +387,7 @@ def get_plan_from_semi_dual(alpha, b, C, regul):
         Ground cost matrix.
     regul: Regularization object
         Should implement a delta_Omega(X) method.
-        
+
     Returns
     -------
     T: array, shape = len(a) x len(b)
@@ -399,20 +399,17 @@ def get_plan_from_semi_dual(alpha, b, C, regul):
 
 def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9,
                    numItermax=500, log=False):
-    
-    if reg_type.lower()=='l2':
+
+    if reg_type.lower() == 'l2':
         regul = SquaredL2(gamma=reg)
-    elif reg_type.lower() in ['entropic','negentropy','kl']:
-        regul = NegEntropy(gamma=reg)    
-    
-    alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax,tol=stopThr)
+    elif reg_type.lower() in ['entropic', 'negentropy', 'kl']:
+        regul = NegEntropy(gamma=reg)
+
+    alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr)
     G = get_plan_from_dual(alpha, beta, M, regul)
-    
+
     if log:
-        log={'alpha':alpha,beta:'beta','res':res}
+        log = {'alpha': alpha, beta: 'beta', 'res': res}
         return G, log
     else:
         return G
-    
-    
-