better init

455-Codage_Sources/algo_code/LBG.py

@@ -1,36 +1,31 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-"""
-Created on Sun May  5 13:59:37 2019
-
-@author: pac
-Algorithme de Linde-Buzo-Gray, version 2D
-"""
-
 import numpy as np
 import matplotlib.pyplot as plt
 
 from scipy.spatial import Voronoi, voronoi_plot_2d
-
-mean= [0,0]
-cov = [[1,0],[0,1]]
+# 
 M = 20;
 N =100; #point par cluster
 K = N*M
 means = np.random.rand(M,2)*10
 X = np.zeros((K,2))
 plt.figure()
-
+cov = np.array([[1,0],[0,1]])
 for m in range(M):
     xi = np.random.multivariate_normal(means[m,:],cov,N)
     X[m*N:(m+1)*N] = xi
     plt.plot(xi[:,0],xi[:,1],'+')
 plt.plot(means[:,0],means[:,1],'ob')
-plt.show()
 
-# X = np.random.multivariate_normal(mean,cov,K)
-Y0 = np.random.multivariate_normal(mean, cov,M)
-Y0 = means;
+mean= np.mean(X,axis=0)
+
+Y0 = np.random.multivariate_normal(mean, 10*cov, M)
+plt.show()
+print(Y0)
+Y0= means
+plt.plot(Y0[:,0],Y0[:,1],'ok')
+
 def LBG(X,Y0,eps=1e-5,maxiter=1000):
     Y = Y0.copy()
     old_dist = np.inf
@@ -45,7 +40,8 @@ def LBG(X,Y0,eps=1e-5,maxiter=1000):
                     cluster_index[k] = j
             dist += sum((X[k]-quant_min)**2)
         for j in range(len(Y)):
-            Y[j,:] = np.mean(X[cluster_index==j],axis=0)            
+            Y[j,:] = np.mean(X[cluster_index==j],axis=0)
+            print(Y)
         if dist-old_dist < eps:
             break
         else:
@@ -56,6 +52,6 @@ vor = Voronoi(Y)
 voronoi_plot_2d(vor,show_vertices=False)
 print(Y)
 plt.plot(X[:,0],X[:,1],'+')
-plt.plot(Y[:,0],Y[:,1],'o')           
-#plt.plot(Y0[:,0],Y0[:,1],'ob')  
+plt.plot(Y[:,0],Y[:,1],'ob')           
+plt.plot(Y0[:,0],Y0[:,1],'ok')  
 plt.show()