Commit 0f84d76d authored by Pierre-antoine Comby's avatar Pierre-antoine Comby

better init

parent 9ed86888
Pipeline #1197 passed with stage
in 3 minutes and 13 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()
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')
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')
# 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
......@@ -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()
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment