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法律研究Page Rank 演算法及使用 Python 的實現
PageRank 演算法可用於網頁。網頁是有向圖，而眾所周知有向圖由節點和連線兩部分組成。頁面就是節點，超連結就是連線，即兩個節點之間的連線。
我們可以透過 PageRank 來判斷各個頁面的重要性，並且很準確。PageRank 的值是介於 0 和 1 之間的機率。
圖中各個節點的 PageRank 值取決於與其連線的所有節點的 PageRank 值，且這些節點又週期性地連線到我們想要為其進行排名的那幾個節點，因此我們使用收斂迭代法來為 PageRank 分配值。
示例程式碼
import numpy as np
import scipy as sc
import pandas as pd
from fractions import Fraction
def display_format(my_vector, my_decimal):
return np.round((my_vector).astype(np.float), decimals=my_decimal)
my_dp = Fraction(1,3)
Mat = np.matrix([[0,0,1],
[Fraction(1,2),0,0],
[Fraction(1,2),1,0]])
Ex = np.zeros((3,3))
Ex[:] = my_dp
beta = 0.7
Al = beta * Mat + ((1-beta) * Ex)
r = np.matrix([my_dp, my_dp, my_dp])
r = np.transpose(r)
previous_r = r
for i in range(1,100):
r = Al * r
print (display_format(r,3))
if (previous_r==r).all():
break
previous_r = r
print ("Final:\n", display_format(r,3))
print ("sum", np.sum(r))輸出
[[0.333] [0.217] [0.45 ]] [[0.415] [0.217] [0.368]] [[0.358] [0.245] [0.397]] [[0.378] [0.225] [0.397]] [[0.378] [0.232] [0.39 ]] [[0.373] [0.232] [0.395]] [[0.376] [0.231] [0.393]] [[0.375] [0.232] [0.393]] [[0.375] [0.231] [0.394]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] [[0.375] [0.231] [0.393]] Final: [[0.375] [0.231] [0.393]] sum 0.9999999999999951
更新於: 2020-06-26
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