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mise en forme du code

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Pierre-antoine Comby 2019-05-05 15:50:24 +02:00
parent 0f84d76db2
commit c9e82a395d
3 changed files with 12 additions and 19 deletions
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6
455-Codage_Sources/Cours/chapA.tex
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@ -14,10 +14,14 @@
\section{Quantification} \section{Quantification}
\subsection{Quantification uniforme} \subsection{Quantification uniforme}
\inputminted{python}{../algo_code/quantif.py} \inputminted{python}{../algo_code/quantif.py}
\newpage
\subsection{Algorithme de Llyod-max} \subsection{Algorithme de Llyod-max}
\inputminted{python}{../algo_code/llyod_max.py} \inputminted{python}{../algo_code/llyod_max.py}
\newpage
\subsection{Algorithme LBG} \subsection{Algorithme LBG}
en 2D , ne pas essayer de tracer les cellule de voronoi \inputminted{python}{../algo_code/LBG.py}
\section{Codeur prédictif} \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. 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} \section{KLT}

14
455-Codage_Sources/algo_code/LBG.py
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@ -2,9 +2,8 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from scipy.spatial import Voronoi, voronoi_plot_2d from scipy.spatial import Voronoi, voronoi_plot_2d
# #initialisations clusters
M = 20; M = 20;
N =100; #point par cluster N =100; #point par cluster
K = N*M K = N*M
@ -17,15 +16,12 @@ for m in range(M):
X[m*N:(m+1)*N] = xi X[m*N:(m+1)*N] = xi
plt.plot(xi[:,0],xi[:,1],'+') plt.plot(xi[:,0],xi[:,1],'+')
plt.plot(means[:,0],means[:,1],'ob') plt.plot(means[:,0],means[:,1],'ob')
mean= np.mean(X,axis=0) mean= np.mean(X,axis=0)
Y0 = np.random.multivariate_normal(mean, 10*cov, M) Y0 = np.random.multivariate_normal(mean, 10*cov, M)
plt.show() plt.show()
print(Y0) Y0= means #triche
Y0= means
plt.plot(Y0[:,0],Y0[:,1],'ok') plt.plot(Y0[:,0],Y0[:,1],'ok')
plt.show()
def LBG(X,Y0,eps=1e-5,maxiter=1000): def LBG(X,Y0,eps=1e-5,maxiter=1000):
Y = Y0.copy() Y = Y0.copy()
old_dist = np.inf old_dist = np.inf
@ -41,16 +37,14 @@ def LBG(X,Y0,eps=1e-5,maxiter=1000):
dist += sum((X[k]-quant_min)**2) dist += sum((X[k]-quant_min)**2)
for j in range(len(Y)): 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: if dist-old_dist < eps:
break break
else: else:
old_dist = dist old_dist = dist
return Y return Y
Y = LBG(X,Y0) Y = LBG(X,Y0)
vor = Voronoi(Y) vor = Voronoi(Y)# black magic
voronoi_plot_2d(vor,show_vertices=False) voronoi_plot_2d(vor,show_vertices=False)
print(Y)
plt.plot(X[:,0],X[:,1],'+') plt.plot(X[:,0],X[:,1],'+')
plt.plot(Y[:,0],Y[:,1],'ob') plt.plot(Y[:,0],Y[:,1],'ob')
plt.plot(Y0[:,0],Y0[:,1],'ok') plt.plot(Y0[:,0],Y0[:,1],'ok')

9
455-Codage_Sources/algo_code/llyod_max.py
View file

@ -5,8 +5,7 @@ from scipy.stats import norm
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
def ddp(x): def ddp(x):
mean = 0, mean = 0; sigma = 1
sigma = 1
return norm.pdf(x,mean,sigma) return norm.pdf(x,mean,sigma)
def quant(centroids, X): 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] /integrate.quad(lambda x: ddp(x),bornes[i],bornes[i+1])[0]
bornes[1:-1] = (centroids[:-1]+centroids[1:])/2 bornes[1:-1] = (centroids[:-1]+centroids[1:])/2
err = np.linalg.norm(centroids-old_centroids) err = np.linalg.norm(centroids-old_centroids)
print(err)
if err < eps : if err < eps :
break break
return centroids return centroids
@ -43,10 +41,7 @@ M = 4
X = np.random.normal(0,1,1000) X = np.random.normal(0,1,1000)
centroids = llyodMax(X,M) centroids = llyodMax(X,M)
bornes = (centroids[:-1]+centroids[1:])/2 bornes = (centroids[:-1]+centroids[1:])/2
bornes = np.insert(bornes,0,-np.inf) bornes = np.insert(bornes,0,-np.inf); bornes = np.append(bornes,np.inf)
bornes = np.append(bornes,np.inf)
print(centroids, bornes)
plt.figure() plt.figure()
plt.plot(X) plt.plot(X)
plt.plot(quant(bornes,X)) plt.plot(quant(bornes,X))