import numpy as np
import sympy
from scipy.optimize import fsolve
from sympy import *
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)
g_15,g_16,g_17 = symbols('g_15 g_16 g_17')
g_25, g_26, g_27 = symbols('g_25 g_26 g_27')
g_35, g_36, g_37 = symbols('g_35 g_36 g_37')
g_45, g_46, g_47 = symbols('g_45 g_46 g_47')
G2 = [[g_15,g_16,g_17]
,[g_25,g_26,g_27]
,[g_35,g_36,g_37]
,[g_45,g_46,g_47]]
G = np.concatenate((G1,G2),axis=1)
print(G.shape)
def func(G,H):
'''
'''
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
GH_T = np.array(GH_T)
print(GH_T)
return GH_T
GH_T1 = func(G,H)
s = sympy.solve([GH_T1[0][0],GH_T1[0][1],GH_T1[0][2]],[g_15,g_16,g_17])