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