图的基本算法--深度优先搜索(dfs) 和广度优先搜索(bfs)_基本算法——深度优先搜索(dfs)和广度优先搜索(bfs)-优快云博客

本文详细介绍了图的深度优先搜索（DFS）和广度优先搜索（BFS）两种基本遍历算法。通过邻接矩阵表示图结构，并给出了递归及非递归实现的DFS算法，以及BFS算法的具体实现过程。每种算法都附带了完整的Python代码示例。

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#   图
#           0
#         / | \
#        1  2 - 4
#       /
#      3

m = 999999    # 代表没有连接
a = [[0, 1, 1, m, 1],    # 邻接矩阵表示图
     [1, 0, m, 1, m],
     [1, m, 0, m, 1],
     [m, 1, m, 0, m],
     [1, m, 1, m, 0]]

n = 5            # 总点数
sm = 0           # 访问点计算
book = [0] * n   # 记录已经访问过的点

def dfs(cur):
    """
    :param cur: 当前访问点
    :return:
    """
    global sm
    print('%d' % cur, end=' ')
    sm = sm + 1
    if sm == n:
        return

    for i in range(0, n):
        if a[cur][i] == 1 and book[i] == 0:
            book[i] = 1
            dfs(i)

def dfs_non_recursion(cur):
    """
    :param cur: 当前访问点
    :return:
    """
    s = []
    s.append(cur)       # 栈
    book[cur] = 1
    while len(s) != 0:
        cur = s.pop()
        print('%d' % cur, end=' ')

        for i in range(n-1, -1, -1):  # 为了个递归的打印顺序相同
            if a[cur][i] == 1 and book[i] == 0:
                book[i] = 1
                s.append(i)

def bfs(cur):
    """
    :param cur: 当前访问点
    :return:
    """
    q = []            # 队列
    q.append(cur)
    while len(q) != 0:
        cur = q.pop(0)
        print('%d' % cur, end=' ')

        for i in range(0, n):
            if a[cur][i] == 1 and book[i] == 0:
                book[i] = 1
                q.append(i)



if __name__ == "__main__":
    book[0] = 1
    dfs(0)

    for i in range(n):
        book[i] = 0
    print()

    dfs_non_recursion(0)

    for i in range(n):
        book[i] = 0
    print()

    book[0] = 1
    bfs(0)