数据结构与算法之——图的遍历-深度优先遍历和广度优先遍历-python实现

图的遍历-深度优先遍历和广度优先遍历-python实现

1、深度优先遍历

1.1 原理

暂时先不写，要画图很麻烦

1.2 python实现深度优先遍历

注：调用的栈类在这个文章：https://www.cnblogs.com/yechangxin/articles/16391518.html

# encoding=utf-8
from data_structures_and_algorithms.stock import Stack
"""
深度优先遍历 基于栈
"""
def DFS(graph, starting_point):
    dfs_stock_obj = Stack()  # 主栈
    node_stock_obj = Stack()  # 主栈节点的子节点栈对象
    dfs_stock_obj.push(starting_point)
    used_points_list = []  # 记录已经遍历过的点
    used_points_list.append(starting_point)
    while not dfs_stock_obj.is_empty_stock():  # 一直到所有子、根节点遍历完一次
        _temp_top_value = dfs_stock_obj.pop()  # 取出栈顶元素并删掉，即列表最后一个元素
        node_stock_obj.stack_list = graph[_temp_top_value]
        for j in range(node_stock_obj.size()):  # 有几个子节点就遍历几次
            i = node_stock_obj.get_top()  # 取子节点栈顶，列表最后一位
            if i not in used_points_list:
                dfs_stock_obj.push(i)
                used_points_list.append(i)
            node_stock_obj.pop()
        print(_temp_top_value)


if __name__ == '__main__':
    graph = {
        "0": ["1", "2", "3", "4"],
        "1": ["0"],
        "2": ["0", "9", "10"],
        "3": ["0"],
        "4": ["0", "5", "8"],
        "5": ["4", "6", "7"],
        "6": ["5"],
        "7": ["5"],
        "8": ["4"],
        "9": ["2"],
        "10": ["2"],
    }
    print("深度优先遍历:")
    DFS(graph, '0')

2、广度优先遍历

2.1 原理

暂时先不写，要画图很麻烦

2.2 python实现广度优先遍历

from data_structures_and_algorithms.my_queue import Queue
"""
广度优先遍历
"""

def BFS(graph, starting_point):  # 广度优先遍历,基于队列
    bfs_queue_obj = Queue()  # 主队列
    node_queue_obj = Queue()  # 主队列元素下的子队列
    bfs_queue_obj.enqueue(starting_point)
    used_points_list = []  # 记录已经遍历过的点
    used_points_list.append(starting_point)
    while not bfs_queue_obj.is_empty_queue():  # 一直到所有子、根节点遍历完一次
        _temp_head_value = bfs_queue_obj.dequeue()  # 队列先进先出
        node_queue_obj.queue_list = graph[_temp_head_value]
        for j in range(node_queue_obj.size()):  # 有几个子节点就遍历几次
            i = node_queue_obj.get_head()  # 取子节点队头，列表第一位
            if i not in used_points_list:
                bfs_queue_obj.enqueue(i)
                used_points_list.append(i)
            node_queue_obj.dequeue()
        print(_temp_head_value)


if __name__ == '__main__':
    graph = {
        "0": ["1", "2", "3", "4"],
        "1": ["0"],
        "2": ["0", "9", "10"],
        "3": ["0"],
        "4": ["0", "5", "8"],
        "5": ["4", "6", "7"],
        "6": ["5"],
        "7": ["5"],
        "8": ["4"],
        "9": ["2"],
        "10": ["2"],
    }
    print("广度优先遍历:")
    BFS(graph, '0')