python 汉明码
作者:互联网
import numpy as np
def func(X):
'''
返回G * H_T
'''
H = np.array(
[[0, 0, 0, 1, 1, 1, 1],
[0, 1, 1, 0, 0, 1, 1],
[1, 0, 1, 0, 1, 0, 1]])
G1 = np.eye(4)
G2 = [[X[0], X[1], X[2]]
, [X[3], X[4], X[5]]
, [X[6], X[7], X[8]]
, [X[9], X[10], X[11]]]
G = np.concatenate((G1, G2), axis=1)
GH_T = np.dot(G, np.transpose(H))
m, n = GH_T.shape[0], GH_T.shape[1]
for i in range(m):
for j in range(n):
GH_T[i][j] %= 2 #不进位
GH_T = np.array(GH_T)
GH_T = GH_T.reshape(1,12)
return GH_T
def shuzu(j,len):
'''
将字符串转换为数组
'''
C = np.zeros((len,))
for k in range(0,len):
C[k] = int(j[k])
return C
def func2(e):
'''
返回伴随式s = e * H_T
'''
H = np.array(
[[0, 0, 0, 1, 1, 1, 1],
[0, 1, 1, 0, 0, 1, 1],
[1, 0, 1, 0, 1, 0, 1]])
s_ = np.dot(e,np.transpose(H))
k = s_.shape[0]
for i in range(k):
s_ %= 2 #不进位
return s_
def siranguji(e_dataset):
'''
求每一个满足条件的e的概率
'''
p_mat = np.array([[6/7,1/7]
,[1/7,6/7]])
P_ = []
length = len(e_dataset)
for i in range(0, length):
P = 1
for j in range(0,7):
if e_dataset[i][j] == 1:
P = P * p_mat[0][1]
elif e_dataset[i][j] == 0:
P = P * p_mat[0][0]
P_.append(P)
return P_
C = np.zeros((12,))
for i in range(0,pow(2,12)):
D = np.zeros((1,12))
i = bin(i)
j = i[2:]
j = j.zfill(12)
C = shuzu(j,12)
G = func(C)
if (G== D).all():
X = C #X为满足G * H_T = 0的解
H = np.array(
[[0, 0, 0, 1, 1, 1, 1],
[0, 1, 1, 0, 0, 1, 1],
[1, 0, 1, 0, 1, 0, 1]])
print('构造的固定的校验矩阵H为:')
print(H)
G1 = np.eye(4)
G2 = [[X[0], X[1], X[2]]
, [X[3], X[4], X[5]]
, [X[6], X[7], X[8]]
, [X[9], X[10], X[11]]]
G = np.concatenate((G1, G2), axis=1)
print('相应的输出矩阵G为:')
print(G)
X = [1,0,0,0,1,0,0,1] #输入8位的x
print('输入的x为:')
print(X)
X1 = X[0:4] #其中前4位做一次汉明编码
X2 = X[4:8] #其中后4位做一次汉明编码
C1 = np.dot(X1,G) #C = XG
i = C1.shape[0]
for j in range(i):
C1[j] %= 2 #不进位
C2 = np.dot(X2,G)
i = C2.shape[0]
for j in range(i):
C2[j] %= 2 #不进位
C = np.concatenate((C1,C2),axis=0)
print('生成的C为:')
print(C)
r = [0,0,0,0,0,1,1,1,0,0,1,1,0,1]
r1 = r[0:7] #前7位求一次伴随式
r2 = r[7:] #后7位求一次伴随式
s1 = np.dot(r1,np.transpose(H))
i = s1.shape[0]
for j in range(i):
s1[j] %= 2 #不进位
s2 = np.dot(r2, np.transpose(H))
i = s2.shape[0]
for j in range(i):
s2[j] %= 2 #不进位
s = np.concatenate((s1,s2),axis=0)
print('s为:')
print(s)
e_dataset1 = []
e_dataset2 = []
for i in range(0,pow(2,7)):
i = bin(i)
j = i[2:]
j = j.zfill(7)
e = shuzu(j,7)
s_ = func2(e)
if (s_ == s1).all():
e_dataset1.append(e) #求满足条件的e
if (s_ == s2).all():
e_dataset2.append(e)
P_1 = siranguji(e_dataset1)
index1 = np.argmax(P_1)
e_1 = np.array(e_dataset1[index1],dtype=int) #求概率最大的e 前面的
P_2 = siranguji(e_dataset2)
index2 = np.argmax(P_2)
e_2 = np.array(e_dataset2[index2],dtype=int) #求概率最大的e 后面的
e_ = np.concatenate((e_1,e_2),axis=0)
print('r为:')
print(r)
print('e为:')
print(e_)
for j in range(0,7):
r_ = np.bitwise_xor(r,e_)
print('c的估计为:')
print(r_)
标签:python,shape,print,range,np,array,汉明码,GH 来源: https://blog.csdn.net/Emanon666/article/details/121070340