From 142f51c080572dd2bfe2b2e9434df648fd7ab018 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Mon, 24 Oct 2016 16:32:01 +0200
Subject: correction barcenter

---
 examples/demo_OT_2D_samples.py |  2 +-
 ot/__init__.py                 |  4 ++--
 ot/bregman.py                  | 18 +++++++++++-------
 3 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py
index f91bbb2..992352c 100644
--- a/examples/demo_OT_2D_samples.py
+++ b/examples/demo_OT_2D_samples.py
@@ -62,7 +62,7 @@ pl.title('OT matrix')
 
 #%% sinkhorn
 
-lambd=.8e-1
+lambd=1e-1
 Gs=ot.sinkhorn(a,b,M,lambd)
 
 
diff --git a/ot/__init__.py b/ot/__init__.py
index ce9d157..dd74590 100644
--- a/ot/__init__.py
+++ b/ot/__init__.py
@@ -8,9 +8,9 @@ import bregman
 
 # OT functions
 from emd import emd
-from bregman import sinkhorn
+from bregman import sinkhorn,barycenter
 
 # utils functions
 from utils import dist,unif
 
-__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif']
+__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif','barycenter']
diff --git a/ot/bregman.py b/ot/bregman.py
index 1761029..e46506b 100644
--- a/ot/bregman.py
+++ b/ot/bregman.py
@@ -116,7 +116,7 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict
 
     #compute Mmax once for all
     #M = M/np.median(M) # suggested by G. Peyre
-    K = np.exp(-reg*M)
+    K = np.exp(-M/reg)
 
     cpt = 0
     err=1
@@ -124,7 +124,8 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict
     UKv=np.dot(K,np.divide(A.T,np.sum(K,axis=0)).T)
     u = (geometricMean(UKv)/UKv.T).T
     
-    log = {'niter':0, 'all_err':[]}
+    log['niter']=0
+    log['all_err']=[]
     
     while (err>tol_error and cpt<numItermax):
         cpt = cpt +1
@@ -149,16 +150,19 @@ def unmixBregman(distrib,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, tol_error=1
         alpha : how much should we trust the prior ? ([0,1])
     """
         
-    M = M/np.median(M) 
-    K = np.exp(-reg*M)
+    #M = M/np.median(M) 
+    K = np.exp(-M/reg)
  
-    M0 = M0/np.median(M0)  
-    K0 = np.exp(-reg0*M0)
+    #M0 = M0/np.median(M0)  
+    K0 = np.exp(-M0/reg0)
     old = h0
 
     err=1
     cpt=0    
-    log = {'niter':0, 'all_err':[]}
+    #log = {'niter':0, 'all_err':[]}
+    log['niter']=0
+    log['all_err']=[]
+    
     
     while (err>tol_error and cpt<numItermax):
         K = projC(K,distrib)  
-- 
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