From f70aabfcc11f92181e0dc987b341bad8ec030d75 Mon Sep 17 00:00:00 2001
From: tvayer <titouan.vayer@gmail.com>
Date: Wed, 29 May 2019 14:16:23 +0200
Subject: pep8

---
 ot/optim.py | 59 +++++++++++++++++++++++++++++------------------------------
 1 file changed, 29 insertions(+), 30 deletions(-)

(limited to 'ot/optim.py')

diff --git a/ot/optim.py b/ot/optim.py
index 9fce21e..cbfb187 100644
--- a/ot/optim.py
+++ b/ot/optim.py
@@ -71,8 +71,9 @@ def line_search_armijo(f, xk, pk, gfk, old_fval,
 
     return alpha, fc[0], phi1
 
-def do_linesearch(cost,G,deltaG,Mi,f_val,
-                  amijo=False,C1=None,C2=None,reg=None,Gc=None,constC=None,M=None):
+
+def do_linesearch(cost, G, deltaG, Mi, f_val,
+                  amijo=False, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None):
     """
     Solve the linesearch in the FW iterations
     Parameters
@@ -119,22 +120,22 @@ def do_linesearch(cost,G,deltaG,Mi,f_val,
     """
     if amijo:
         alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val)
-    else: # requires symetric matrices
-        dot1=np.dot(C1,deltaG) 
-        dot12=dot1.dot(C2) 
-        a=-2*reg*np.sum(dot12*deltaG)
-        b=np.sum((M+reg*constC)*deltaG)-2*reg*(np.sum(dot12*G)+np.sum(np.dot(C1,G).dot(C2)*deltaG)) 
-        c=cost(G) 
+    else:  # requires symetric matrices
+        dot1 = np.dot(C1, deltaG)
+        dot12 = dot1.dot(C2)
+        a = -2 * reg * np.sum(dot12 * deltaG)
+        b = np.sum((M + reg * constC) * deltaG) - 2 * reg * (np.sum(dot12 * G) + np.sum(np.dot(C1, G).dot(C2) * deltaG))
+        c = cost(G)
+
+        alpha = solve_1d_linesearch_quad_funct(a, b, c)
+        fc = None
+        f_val = cost(G + alpha * deltaG)
 
-        alpha=solve_1d_linesearch_quad_funct(a,b,c)
-        fc=None
-        f_val=cost(G+alpha*deltaG)
-        
-    return alpha,fc,f_val
+    return alpha, fc, f_val
 
 
 def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
-       stopThr=1e-9, verbose=False, log=False,**kwargs):
+       stopThr=1e-9, verbose=False, log=False, **kwargs):
     """
     Solve the general regularized OT problem with conditional gradient
 
@@ -240,7 +241,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
         deltaG = Gc - G
 
         # line search
-        alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc,**kwargs)
+        alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs)
 
         G = G + alpha * deltaG
 
@@ -403,11 +404,12 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
     else:
         return G
 
-def solve_1d_linesearch_quad_funct(a,b,c):
+
+def solve_1d_linesearch_quad_funct(a, b, c):
     """
-    Solve on 0,1 the following problem: 
+    Solve on 0,1 the following problem:
     .. math::
-        \min f(x)=a*x^{2}+b*x+c 
+        \min f(x)=a*x^{2}+b*x+c
 
     Parameters
     ----------
@@ -416,22 +418,19 @@ def solve_1d_linesearch_quad_funct(a,b,c):
 
     Returns
     -------
-    x : float 
+    x : float
         The optimal value which leads to the minimal cost
-           
+
     """
-    f0=c
-    df0=b
-    f1=a+f0+df0
+    f0 = c
+    df0 = b
+    f1 = a + f0 + df0
 
-    if a>0: # convex
-        minimum=min(1,max(0,-b/(2*a)))
-        #print('entrelesdeux')
+    if a > 0:  # convex
+        minimum = min(1, max(0, -b / (2 * a)))
         return minimum
-    else: # non convexe donc sur les coins
-        if f0>f1:
-            #print('sur1 f(1)={}'.format(f(1)))            
+    else:  # non convex
+        if f0 > f1:
             return 1
         else:
-            #print('sur0 f(0)={}'.format(f(0)))
             return 0
-- 
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