add classes for entropic and group lasso regularization

Author: rflamary

ot/da.py

@@ -210,8 +210,8 @@ def predict(self,x,direction=1):
 
 
 class OTDA_sinkhorn(OTDA):
-    
-    def fit(self,xs,xt,ws=None,wt=None,reg=1,**kwargs):
+    """Class for domain adaptation with optimal transport with entropic regularization"""
+    def fit(self,xs,xt,reg=1,ws=None,wt=None,**kwargs):
         """ Fit domain adaptation between samples is xs and xt (with optional 
             weights)"""
         self.xs=xs
@@ -230,5 +230,26 @@ def fit(self,xs,xt,ws=None,wt=None,reg=1,**kwargs):
         self.computed=True    
 
 
+class OTDA_lpl1(OTDA):
+    """Class for domain adaptation with optimal transport with entropic an group regularization"""
+    
 
+    def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs):
+        """ Fit domain adaptation between samples is xs and xt (with optional 
+            weights)"""
+        self.xs=xs
+        self.xt=xt
+        
+        if wt is None:
+            wt=unif(xt.shape[0])
+        if ws is None:
+            ws=unif(xs.shape[0])
+            
+        self.ws=ws
+        self.wt=wt
+            
+        self.M=dist(xs,xt,metric=self.metric)
+        self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs)
+        self.computed=True    
+