add log and epsilon scaling stabilizations

1 files changed, 5 insertions, 5 deletions

diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py
index 5e19be5..0a8c583 100644
--- a/examples/demo_optim_OTreg.py
+++ b/examples/demo_optim_OTreg.py
@@ -17,8 +17,8 @@ n=100 # nb bins
 x=np.arange(n,dtype=np.float64)
 
 # Gaussian distributions
-a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std
-b=ot.datasets.get_1D_gauss(n,m=60,s=60)
+a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std
+b=ot.datasets.get_1D_gauss(n,m=60,s=10)
 
 # loss matrix
 M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))
@@ -37,7 +37,7 @@ def f(G): return 0.5*np.sum(G**2)
 def df(G): return G
 
 reg=1e-1
-  
+
 Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)
 
 pl.figure(3)
@@ -47,9 +47,9 @@ ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')
 
 def f(G): return np.sum(G*np.log(G))
 def df(G): return np.log(G)+1
-    
+
 reg=1e-3
-  
+
 Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True)
 
 pl.figure(4)