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迭代法计算一般定义的PageRank(原生Python+numpy矩阵计算实现).py
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迭代法计算一般定义的PageRank(原生Python+numpy矩阵计算实现).py
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import numpy as np
def pagerank_1(M, d=0.8, tol=1e-8, max_iter=1000):
"""PageRank的迭代算法
:param M: 转移概率矩阵
:param d: 阻尼因子
:param tol: 容差
:param max_iter: 最大迭代次数
:return: PageRank值(平稳分布)
"""
n_components = len(M)
# 初始状态分布:均匀分布
pr0 = np.array([1 / n_components] * n_components)
# 迭代寻找平稳状态
for _ in range(max_iter):
pr1 = d * np.dot(M, pr0) + (1 - d) / n_components
# 判断迭代更新量是否小于容差
if np.sum(np.abs(pr0 - pr1)) < tol:
break
pr0 = pr1
return pr0
if __name__ == "__main__":
np.set_printoptions(precision=2, suppress=True)
P = np.array([[0, 1 / 2, 0, 0],
[1 / 3, 0, 0, 1 / 2],
[1 / 3, 0, 0, 1 / 2],
[1 / 3, 1 / 2, 0, 0]])
print(pagerank_1(P)) # [0.1 0.13 0.13 0.13]
P = np.array([[0, 1 / 2, 0, 0],
[1 / 3, 0, 0, 1 / 2],
[1 / 3, 0, 1, 1 / 2],
[1 / 3, 1 / 2, 0, 0]])
print(pagerank_1(P)) # [0.1 0.13 0.64 0.13]
P = np.array([[0, 0, 1],
[1 / 2, 0, 0],
[1 / 2, 1, 0]])
print(pagerank_1(P)) # [0.38 0.22 0.4 ]