have corect normalization for entropic mapping

Author: rflamary

ot/da.py

@@ -493,7 +493,7 @@ class label
 
             # pairwise distance
             self.cost_ = dist(Xs, Xt, metric=self.metric)
-            self.cost_ = cost_normalization(self.cost_, self.norm)
+            self.cost_, self.norm_cost_ = cost_normalization(self.cost_, self.norm, return_value=True)
 
             if (ys is not None) and (yt is not None):
 
@@ -1209,7 +1209,7 @@ class label
             g = self.log_['log_v']
 
             M = dist(Xs, self.xt_, metric=self.metric)
-            M = cost_normalization(M, self.norm)
+            M = cost_normalization(M, self.norm, value=self.norm_cost_)
 
             K = nx.exp(-M / self.reg_e + g[None, :])
 
@@ -1253,7 +1253,7 @@ class label
             f = self.log_['log_u']
 
             M = dist(Xt, self.xs_, metric=self.metric)
-            M = cost_normalization(M, self.norm)
+            M = cost_normalization(M, self.norm, value=self.norm_cost_)
 
             K = nx.exp(-M / self.reg_e + f[None, :])
 

ot/utils.py

@@ -360,7 +360,7 @@ def dist0(n, method='lin_square'):
     return res
 
 
-def cost_normalization(C, norm=None):
+def cost_normalization(C, norm=None, return_value=False, value=None):
     r""" Apply normalization to the loss matrix
 
     Parameters
@@ -382,9 +382,13 @@ def cost_normalization(C, norm=None):
     if norm is None:
         pass
     elif norm == "median":
-        C /= float(nx.median(C))
+        if value is None:
+            value = nx.median(C)
+        C /= value
     elif norm == "max":
-        C /= float(nx.max(C))
+        if value is None:
+            value = nx.max(C)
+        C /= float(value)
     elif norm == "log":
         C = nx.log(1 + C)
     elif norm == "loglog":
@@ -393,7 +397,10 @@ def cost_normalization(C, norm=None):
         raise ValueError('Norm %s is not a valid option.\n'
                          'Valid options are:\n'
                          'median, max, log, loglog' % norm)
-    return C
+    if return_value:
+        return C, value
+    else:
+        return C
 
 
 def dots(*args):