networkX

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networkX

文章目录

• 1.构建图
• 2.使用networkX查找最短路径
• 3.自己构建方法

import networkx as nx
import matplotlib.pyplot as plt

1.构建图

# 创建有向图
G = nx.DiGraph()# 添加带权重的边
edges = [(0, 1, 1), (0, 2, 2), (1, 2, 1), (1, 3, 2), (2, 3, 1),(3, 4, 3), (2, 4, 4), (4, 5, 2), (3, 5, 5), 
]
G.add_weighted_edges_from(edges)# 绘制图
pos = nx.spring_layout(G)  # 使用Spring布局
nx.draw(G, pos, with_labels=True, node_size=2000, node_color="lightblue", font_size=10)
nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): G[u][v]['weight'] for u, v in G.edges()}, font_color='red')# 显示图
plt.show()

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2.使用networkX查找最短路径

from itertools import islice
def k_shortest_paths(G, source, target, k, weight=None):return list(islice(nx.shortest_simple_paths(G, source, target, weight=weight), k))

# 获取 k-最短路径
paths = k_shortest_paths(G, 0, 5, 3, 'weight')# 输出路径和权重
for i, path in enumerate(paths):weight = sum(G[path[n]][path[n + 1]]['weight'] for n in range(len(path) - 1))print(f"Path {i + 1}: {path}, weight: {weight}")

Path 1: [0, 1, 3, 5], weight: 8
Path 2: [0, 2, 3, 5], weight: 8
Path 3: [0, 1, 2, 3, 5], weight: 8

3.自己构建方法

from itertools import count
from heapq import heappush, heappop
import networkx as nx
import pandas as pd 
import matplotlib.pyplot as pltclass K_shortest_path(object):def __init__(self,G,  k=3, weight='weight') -> None:self.G = G self.k = kself.weight = weightself.G_original = Gdef get_path_length(self,G,path:list, weight='weight'):"""计算每条路径的总阻抗，基于weightArgs:G (nx.graph): 构建的图path (list): 路径weight (str, optional): 边的权重计算基于什么，可以是时间也可以是距离. Defaults to 'weight'."""length = 0if len(path) > 1:for i in range(len(path) - 1):u = path[i]v = path[i + 1]length += G.edges[u,v].get(weight, 1)return length     def find_sp(self,s,t,G):"""找到第一条P(1)Args:s (node): 路径起点t (node): 路径终点lenght:P(1)对应的长度path:P(1)对应的路径 list"""path_1 = nx.shortest_path(G=G,source=s,target=t,weight=self.weight)length_1 = nx.shortest_path_length(G=G,source=s,target=t,weight=self.weight)# length_1, path_1 = nx.single_source_dijkstra(G,source=s,weight=weight)return length_1, path_1def find_Pi_sp(self,source,target):if source == target:return ([0], [[source]]) G =  self.Gk = self.klength, path = self.find_sp(G=G,s=source,t=target)lengths = []paths = []lengths.append(length)paths.append(path)c = count()        B = [] G_original = self.G.copy()   for i in range(1, k):for j in range(len(paths[-1]) - 1):            spur_node = paths[-1][j]root_path = paths[-1][:j + 1]edges_removed = []for c_path in paths:if len(c_path) > j and root_path == c_path[:j + 1]:u = c_path[j] #节点v = c_path[j + 1] #节点if G.has_edge(u, v):  #查看u，v节点之间是否有路径edge_attr = G.edges[u,v]['weight']G.remove_edge(u, v) #移除边edges_removed.append((u, v, edge_attr))for n in range(len(root_path) - 1):node = root_path[n]# out-edgesdict_d = []for (u,v,edge_attr) in G.edges(nbunch =node,data = True ):# for u, v, edge_attr in G.edges_iter(node, data=True):edge_attr = edge_attr['weight']dict_d.append((u,v))edges_removed.append((u, v, edge_attr))G.remove_edges_from(dict_d) if G.is_directed():# in-edgesin_edges_d_list = []for (u,v,edge_attr) in G.edges(nbunch =node,data = True ):# for u, v, edge_attr in G.in_edges_iter(node, data=True):# edge_attr = edge_attr['weight']edge_attr = G.edges[u,v]['weight']# G.remove_edge(u, v)in_edges_d_list.append((u,v))edges_removed.append((u, v, edge_attr))G.remove_edges_from(in_edges_d_list) spur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, weight=self.weight)            if target in spur_path and spur_path[target]:total_path = root_path[:-1] + spur_path[target]total_path_length = self.get_path_length(G_original, root_path, self.weight) + spur_path_length[target]                heappush(B, (total_path_length, next(c), total_path))for e in edges_removed:u, v, edge_attr = eG.add_edge(u, v, weight = edge_attr)if B:(l, _, p) = heappop(B)        lengths.append(l)paths.append(p)else:breakreturn (lengths,paths)

  
if __name__ =='__main__':# 创建有向图G = nx.DiGraph()# 添加带权重的边edges = [(0, 1, 1), (0, 2, 2), (1, 2, 1), (1, 3, 2), (2, 3, 1),(3, 4, 3), (2, 4, 4), (4, 5, 2), (3, 5, 5), ]G.add_weighted_edges_from(edges)for u, v, weight in edges:G.add_edge(u, v, weight=weight)KSP = K_shortest_path(G=G,k=3,weight='weight')KSP.G# 绘制图pos = nx.spring_layout(KSP.G)  # 使用Spring布局nx.draw(KSP.G, pos, with_labels=True, node_size=2000, node_color="lightblue", font_size=10)nx.draw_networkx_edge_labels(KSP.G, pos, edge_labels={(u, v): KSP.G[u][v]['weight'] for u, v in KSP.G.edges()}, font_color='red')# 显示图plt.show()# 最短路径查询source = 0target = 5(lengths,paths) = KSP.find_Pi_sp(source=source,target=target)k_df = pd.DataFrame((lengths,paths)).Tk_df.columns = ['weight','path']print(k_df)

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  weight             path
0      8     [0, 1, 3, 5]
1      8     [0, 2, 3, 5]
2      8  [0, 1, 2, 3, 5]

本文标签： networkX