Commit 9ed86888 authored by Pierre-antoine Comby's avatar Pierre-antoine Comby

LBG algo

parent bc09d956
Pipeline #1196 passed with stage
in 3 minutes and 14 seconds
#!/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()
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;
def LBG(X,Y0,eps=1e-5,maxiter=1000):
Y = Y0.copy()
old_dist = np.inf
cluster_index = np.zeros(K,dtype=int)
for l in range(maxiter):
dist= 0;
for k in range(len(X)):
quant_min =np.inf
for j in range(len(Y)):
if np.linalg.norm(X[k]-Y[j]) <np.linalg.norm(X[k]-quant_min):
quant_min = Y[j]
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)
if dist-old_dist < eps:
break
else:
old_dist = dist
return Y
Y = LBG(X,Y0)
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.show()
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