Python 实现的PageRank算法，纯粹使用python原生模块，没有使用numpy、scipy。这个程序实现还比较原始，可优化的地方较多。
#-*- coding:utf-8 -*-
import random
N = 8 #八个网页
d = 0.85 #阻尼因子为0.85
delt = 0.00001 #迭代控制变量
#两个矩阵相乘
def matrix_multi(A,B):
result = [[0]*len(B[0]) for i in range(len(A))]
for i in range(len(A)):
for j in range(len(B[0])):
for k in range(len(B)):
result[i][j] += A[i][k]*B[k][j]
return result
#矩阵A的每个元素都乘以n
def matrix_multiN(n,A):
result = [[1]*len(A[0]) for i in range(len(A))]
for i in range(len(A)):
for j in range(len(A[0])):
result[i][j] = n*A[i][j]
return result
#两个矩阵相加
def matrix_add(A,B):
if len(A[0])!=len(B[0]) and len(A)!=len(B):
return
result = [[0]*len(A[0]) for i in range(len(A))]
for i in range(len(A)):
for j in range(len(A[0])):
result[i][j] = A[i][j]+B[i][j]
return result
def pageRank(A):
e = []
for i in range(N):
e.append(1)
norm = 100
New_P = []
for i in range(N):
New_P.append([random.random()])
r = [ [(1-d)*i*1/N] for i in e]
while norm > delt:
P = New_P
New_P = matrix_add(r,matrix_multiN(d,matrix_multi(A,P))) #P=(1-d)*e/n+d*M'P PageRank算法的核心
norm = 0
#求解矩阵一阶范数
for i in range(N):
norm += abs(New_P[i][0]-P[i][0])
print New_P
#根据邻接矩阵求转移概率矩阵并转向
def tran_and_convert(A):
result = [[0]*len(A[0]) for i in range(len(A))]
result_convert = [[0]*len(A[0]) for i in range(len(A))]
for i in range(len(A)):
for j in range(len(A[0])):
result[i][j] = A[i][j]*1.0/sum(A[i])
for i in range(len(result)):
for j in range(len(result[0])):
result_convert[i][j]=result[j][i]
return result_convert
def main():
A = [[0,1,1,0,0,1,0,0],\
[0,0,0,1,1,0,0,0],\
[0,0,0,1,0,1,0,0],\
[0,0,0,0,0,1,0,0],\
[1,0,0,1,0,0,1,1],\
[0,0,0,1,0,0,0,0],\
[0,0,1,0,0,0,0,0],\
[0,0,0,1,0,0,1,0]]
M = tran_and_convert(A)
pageRank(M)
if __name__ == '__main__':
main()
