vers = input() file_text = "text.txt" with open(file_text, 'r') as f: content = f.readlines() vers_num = content[0] edges = [content[1], content[2], content[3], content[4]] g = LinkedGraph(vers, edges) g.print() G = nx.DiGraph() G.add_edges_from([(1, 2), (2, 3), (2, 4), (3, 4)])编写函数函数计算G各节点PageRank值并实现可视化不使用nx函数

以下是一个计算给定有向图G各节点PageRank值并实现可视化的Python函数，不使用`networkx`库：

import numpy as np
import matplotlib.pyplot as plt

class LinkedGraph:
    def __init__(self, vers, edges):
        self.vers = int(vers)
        self.edges = []
        for edge in edges:
            u, v = map(int, edge.split())
            self.edges.append((u, v))
    
    def print(self):
        print("Number of vertices:", self.vers)
        print("Edges:")
        for edge in self.edges:
            print(edge[0], "->", edge[1])

def pagerank(G, d=0.85, eps=1.0e-8, max_iter=100):
    n = len(G)
    A = np.zeros((n,n))
    for e in G.edges():
        A[e[1],e[0]] = 1.0 / G.out_degree(e[0])
    v = np.ones(n) / n
    for i in range(max_iter):
        v_new = np.zeros(n)
        for j in range(n):
            for i in G.predecessors(j):
                v_new[j] += A[j,i] * v[i]
            v_new[j] = d * v_new[j] + (1 - d) / n
        if np.abs(v - v_new).max() < eps:
            break
        v = v_new
    return v

# Test the function with an example
vers = 4
edges = [(1, 2), (2, 3), (2, 4), (3, 4)]
G = LinkedGraph(vers, edges)
pr = pagerank(G)
print(pr)

# Visualize the PageRank values
plt.bar(range(len(pr)), pr)
plt.xlabel('Node')
plt.ylabel('PageRank')
plt.show()

在这个函数中，`LinkedGraph`类将给定的节点数和边列表转换为有向图。`pagerank`函数采用一个有向图 `G` 作为输入，使用矩阵运算来计算每个节点的PageRank值，并返回节点PageRank值数组。最后，函数使用Matplotlib库可视化PageRank值。