-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathPageRank.py
99 lines (88 loc) · 3.67 KB
/
PageRank.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
import matplotlib
import networkx as nx
import numpy as np
from PowerIterator import PowerIterator
a = 0
b = 0
c = 1 / 3
d = 1 / 2
e = 1 / 2
f = 1
g = 0
h = 1
i = 1 / 2
j = 1 / 3
k = 1 / 2
l = 1 / 2
m = 1 / 2
n = 1 / 2
o = 1 / 2
M = np.array([
[0, 0, 0, d, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, j, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, i, j, 0, 0, 0, 0, o],
[0, 0, 0, 0, e, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, c, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, d, 0, f, 0, 0, 0, 0, 0, 0, m, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, k, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, h, 0, j, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, l, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, k, 0, 0, n, 0],
[0, 0, c, 0, 0, 0, 0, 0, i, 0, 0, 0, 0, 0, 0],
[0, 0, 0, d, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, o],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, l, 0, 0, 0],
[0, 0, c, 0, e, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, m, n, 0]
])
N_1 = np.array([
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15],
[1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15, 1 / 15,
1 / 15]
])
class PageRank:
def __init__(self, m_matrix, n_matrix, betha, betha_1):
self.m_matrix = m_matrix
self.n_matrix = n_matrix
self.betha = betha
self.betha_1 = betha_1
def plot_graph(self):
nx.draw(G, with_labels=True)
matplotlib.pyplot.show()
def google_matrix(self):
m_betha = np.dot(self.betha, self.m_matrix)
m_betha_1 = np.dot(self.betha_1, self.n_matrix)
return m_betha + m_betha_1
if __name__ == '__main__':
page_rank = PageRank(m_matrix=M, n_matrix=N_1, betha=0.80, betha_1=0.20)
G = nx.DiGraph(np.array(M).transpose())
page_rank.plot_graph()
A = page_rank.google_matrix()
power_iterator = PowerIterator(M, N_1[0], G, A)
power_iterator.run()