sinkhorn GPU implementation

1 files changed, 11 insertions, 12 deletions

diff --git a/ot/da.py b/ot/da.py
index 81b6a35..d7b8492 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -193,30 +193,30 @@ def sinkhorn_l1l2_gl(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter
 
     """
     lstlab=np.unique(labels_a)
-                
+
     def f(G):
         res=0
         for i in range(G.shape[1]):
             for lab in lstlab:
                 temp=G[labels_a==lab,i]
-                res+=np.linalg.norm(temp)           
+                res+=np.linalg.norm(temp)
         return res
-        
+
     def df(G):
-        W=np.zeros(G.shape)    
+        W=np.zeros(G.shape)
         for i in range(G.shape[1]):
             for lab in lstlab:
                 temp=G[labels_a==lab,i]
                 n=np.linalg.norm(temp)
                 if n:
-                    W[labels_a==lab,i]=temp/n 
-        return W   
+                    W[labels_a==lab,i]=temp/n
+        return W
+
 
-            
     return gcg(a,b,M,reg,eta,f,df,G0=None,numItermax = numItermax,numInnerItermax=numInnerItermax, stopThr=stopInnerThr,verbose=verbose,log=log)
-    
-    
-    
+
+
+
 def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs):
     """Joint OT and linear mapping estimation as proposed in [8]
 
@@ -685,7 +685,6 @@ class OTDA(object):
         return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation
 
 
-
 class OTDA_sinkhorn(OTDA):
     """Class for domain adaptation with optimal transport with entropic regularization"""
 
@@ -727,7 +726,7 @@ class OTDA_lpl1(OTDA):
         self.M=dist(xs,xt,metric=self.metric)
         self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs)
         self.computed=True
-        
+
 class OTDA_l1l2(OTDA):
     """Class for domain adaptation with optimal transport with entropic and group lasso regularization"""