diff --git a/455-Codage_Sources/Cours/chapA.tex b/455-Codage_Sources/Cours/chapA.tex
index 7b11b0f..35b0cf3 100644
--- a/455-Codage_Sources/Cours/chapA.tex
+++ b/455-Codage_Sources/Cours/chapA.tex
@@ -14,10 +14,14 @@
 \section{Quantification}
 \subsection{Quantification uniforme}
 \inputminted{python}{../algo_code/quantif.py}
+\newpage
 \subsection{Algorithme de Llyod-max}
 \inputminted{python}{../algo_code/llyod_max.py}
+\newpage
 \subsection{Algorithme LBG}
-en 2D , ne pas essayer de tracer les cellule de voronoi
+\inputminted{python}{../algo_code/LBG.py}
+
+
 \section{Codeur prédictif}
 Construire un schéma de prédiction en boucle fermée à fenêtre glissante dasn lequel $M$et  $p$ sont paramétrisable. On utilisera un quantificateur à zone morte de pas $\Delta$ paramétrisable.
 \section{KLT}
diff --git a/455-Codage_Sources/algo_code/LBG.py b/455-Codage_Sources/algo_code/LBG.py
index 35b36f2..0291353 100755
--- a/455-Codage_Sources/algo_code/LBG.py
+++ b/455-Codage_Sources/algo_code/LBG.py
@@ -2,9 +2,8 @@
 # -*- coding: utf-8 -*-
 import numpy as np
 import matplotlib.pyplot as plt
-
 from scipy.spatial import Voronoi, voronoi_plot_2d
-# 
+#initialisations clusters
 M = 20;
 N =100; #point par cluster
 K = N*M
@@ -17,15 +16,12 @@ for m in range(M):
     X[m*N:(m+1)*N] = xi
     plt.plot(xi[:,0],xi[:,1],'+')
 plt.plot(means[:,0],means[:,1],'ob')
-
 mean= np.mean(X,axis=0)
-
 Y0 = np.random.multivariate_normal(mean, 10*cov, M)
 plt.show()
-print(Y0)
-Y0= means
+Y0= means #triche
 plt.plot(Y0[:,0],Y0[:,1],'ok')
-
+plt.show()
 def LBG(X,Y0,eps=1e-5,maxiter=1000):
     Y = Y0.copy()
     old_dist = np.inf
@@ -41,17 +37,15 @@ def LBG(X,Y0,eps=1e-5,maxiter=1000):
             dist += sum((X[k]-quant_min)**2)
         for j in range(len(Y)):
             Y[j,:] = np.mean(X[cluster_index==j],axis=0)
-            print(Y)
         if dist-old_dist < eps:
             break
         else:
             old_dist = dist
     return Y
 Y = LBG(X,Y0)
-vor = Voronoi(Y) 
+vor = Voronoi(Y)# black magic
 voronoi_plot_2d(vor,show_vertices=False)
-print(Y)
 plt.plot(X[:,0],X[:,1],'+')
 plt.plot(Y[:,0],Y[:,1],'ob')           
 plt.plot(Y0[:,0],Y0[:,1],'ok')  
-plt.show()        
+plt.show()        
\ No newline at end of file
diff --git a/455-Codage_Sources/algo_code/llyod_max.py b/455-Codage_Sources/algo_code/llyod_max.py
index 0c9d1a9..2337d8b 100755
--- a/455-Codage_Sources/algo_code/llyod_max.py
+++ b/455-Codage_Sources/algo_code/llyod_max.py
@@ -5,8 +5,7 @@ from scipy.stats import norm
 import matplotlib.pyplot as plt
 
 def ddp(x):
-    mean = 0,
-    sigma = 1
+    mean = 0; sigma = 1
     return norm.pdf(x,mean,sigma)
 
 def quant(centroids, X):
@@ -34,7 +33,6 @@ def llyodMax(X,M,maxiter=1000,eps=1e-6):
                          /integrate.quad(lambda x: ddp(x),bornes[i],bornes[i+1])[0]
         bornes[1:-1] = (centroids[:-1]+centroids[1:])/2
         err = np.linalg.norm(centroids-old_centroids)
-        print(err)
         if err < eps :
             break
     return centroids    
@@ -43,10 +41,7 @@ M = 4
 X = np.random.normal(0,1,1000)
 centroids = llyodMax(X,M)
 bornes = (centroids[:-1]+centroids[1:])/2
-bornes = np.insert(bornes,0,-np.inf)
-bornes = np.append(bornes,np.inf)
-
-print(centroids, bornes)
+bornes = np.insert(bornes,0,-np.inf); bornes = np.append(bornes,np.inf)
 plt.figure()
 plt.plot(X)
 plt.plot(quant(bornes,X))