commit doc

2 files changed, 38 insertions, 8 deletions

diff --git a/docs/source/conf.py b/docs/source/conf.py
index b56df38..ffc0da8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -14,9 +14,14 @@
 
 import sys
 import os
-from unittest.mock import MagicMock
+try:
+    from unittest.mock import MagicMock
+except ImportError:
+    from mock import MagicMock
 
 sys.path.insert(0, os.path.abspath("../.."))
+sys.setrecursionlimit(1500)
+
 
 
 class Mock(MagicMock):
diff --git a/ot/da.py b/ot/da.py
index 11e420b..87354b9 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -188,21 +188,46 @@ class OTDA():
             return None
         
         
-    def predict(x,direction=1):
+    def predict(self,x,direction=1):
         """ Out of sample mapping using the formulation from Ferradans 
         
+        It basically find the source sample the nearset to the nex sample and 
+        apply the difference to the displaced source sample.
+        
         """
         if direction>0: # >0 then source to target 
-            G=self.G
-            w=self.ws.reshape((self.xs.shape[0],1))
-            x=self.xt
+            xf=self.xt
+            x0=self.xs
         else:
-            G=self.G.T
-            w=self.wt.reshape((self.xt.shape[0],1))
-            x=self.xs        
+            xf=self.xs      
+            x0=self.xt
+            
+        D0=dist(x,x0) # dist netween new samples an source
+        idx=np.argmin(D0,1) # closest one
+        xf=self.interp(direction)# interp the source samples
+        return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation
         
         
+
+class OTDA_sinkhorn(OTDA):
+    
+    def fit(self,xs,xt,ws=None,wt=None,reg=1,**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(ws,wt,self.M,reg,**kwargs)
+        self.computed=True