Shortest path algorithm implementation, complete tutorial on project use cases

Shortest path algorithmIt is a type of algorithm for finding the shortest path in the graph, and is often used in the fields of routing algorithms, map navigation, logistics and distribution. The following is a complete tutorial on the project use case implementation of a shortest path algorithm:

Install the dependency library

First, we need to install a Python graphics library networkx, and aData visualizationLibrary matplotlib:

pip install networkx

pip install matplotlib

Build a graph

We can build graphs through the networkx library. Here we build aDirected graph, and set the weight of each edge to a randomly generated floating point number:

python

import networkx as nx

import random

 

# Build a directed graph

G = ()

 

# Add 100 nodes

for i in range(100):

    G.add_node(i)

 

# Add 500 edges and set weights

for i in range(500):

    u, v = (range(100), 2)

    w = ()

    G.add_edge(u, v, weight=w)

calculateShortest path

Next, we can use shortest_path in the networkx libraryfunctionTo calculate the shortest path between two nodes. Here is a sample code:

python

# Calculate the shortest path from node 0 to node 99

path = nx.shortest_path(G, source=0, target=99, weight='weight')

 

# Output shortest path

print(path)

Visualization

Finally, we can use the matplotlib library to visualize the graph. Here is a sample code:

python

import as plt

 

# Visualize the graphics

pos = nx.spring_layout(G)

(G, pos)

nx.draw_networkx_nodes(G, pos, nodelist=path, node_color='r')

nx.draw_networkx_edges(G, pos, edgelist=list(zip(path, path[1:])), edge_color='r', width=2)

()

After running the code, the shortest path from node 0 to node 99 will be output, and the entire graph will be visualized, and the shortest path will be marked in red.

Summarize

Through the complete tutorial on the project use case implementation of this shortest path algorithm, we learned how to use Python's networkx library and matplotlib library to build graphs and visualizations, and use the shortest_path function to calculate the shortest path. This algorithm can be applied in many scenarios, such asRouting algorithm, map navigation, logistics and distribution, etc.