import numpy as np
from scipy.sparse.linalg import eigs
import pylab as plt
w = np.array([[0, 1, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[1, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 1],
[0, 0, 1, 0, 0, 1],
[0, 0, 1, 0, 0, 0]])
r = np.sum(w,axis=1,keepdims=True)
n = w.shape[0]
d = 0.85
P = (1-d)/n+d*w/r #利用矩陣廣播
w,v = eigs(P.T,1) #求最大特徵值及對應的特徵向量
v = v/sum(v)
v = v.real
print("最大特徵值為：",w.real)
print("歸一化特徵向量為：\n",np.round(v,4))
plt.bar(range(1,n+1),v.flatten(),width=0.6)
plt.show()
print("2023310143005")
