Python实现（1）实现图的邻接矩阵存储，包括建立、输出，输出指定顶点的边信息。 （2）实现图的邻接链表存储，包括建立、输出，输出指定顶点的边信息。 （3）实现将图的领接矩阵转换成邻接表，和将邻接表转换成邻接矩阵。 （4）在两种存储结构上，实现深度遍历算法。 （5）在两种存储结构上，实现广度遍历算法。

好的，下面是Python实现的代码：

1. 图的邻接矩阵存储

class Graph:
    def __init__(self, num_vertices):
        self.adj_matrix = [[0] * num_vertices for _ in range(num_vertices)]
        self.num_vertices = num_vertices

    def add_edge(self, i, j, weight):
        self.adj_matrix[i][j] = weight
        self.adj_matrix[j][i] = weight

    def get_edge(self, i, j):
        return self.adj_matrix[i][j]

    def display(self):
        for i in range(self.num_vertices):
            for j in range(self.num_vertices):
                print(self.adj_matrix[i][j], end=' ')
            print()

2. 图的邻接链表存储

from collections import defaultdict

class Graph:
    def __init__(self):
        self.adj_list = defaultdict(list)

    def add_edge(self, u, v, weight):
        self.adj_list[u].append((v, weight))
        self.adj_list[v].append((u, weight))

    def get_edge(self, u, v):
        for edge in self.adj_list[u]:
            if edge[0] == v:
                return edge[1]
        return None

    def display(self):
        for u in self.adj_list:
            print(u, end=' -> ')
            for v, weight in self.adj_list[u]:
                print(f'{v}({weight})', end=' ')
            print()

3. 将图的领接矩阵转换成邻接表，和将邻接表转换成邻接矩阵

class Graph:
    def __init__(self, num_vertices):
        self.adj_matrix = [[0] * num_vertices for _ in range(num_vertices)]
        self.adj_list = defaultdict(list)
        self.num_vertices = num_vertices

    def add_edge(self, i, j, weight):
        self.adj_matrix[i][j] = weight
        self.adj_matrix[j][i] = weight
        self.adj_list[i].append((j, weight))
        self.adj_list[j].append((i, weight))

    def matrix_to_list(self):
        return self.adj_list

    def list_to_matrix(self):
        for u in self.adj_list:
            for v, weight in self.adj_list[u]:
                self.adj_matrix[u][v] = weight
                self.adj_matrix[v][u] = weight
        return self.adj_matrix

4. 图的深度遍历算法

class Graph:
    def __init__(self, num_vertices):
        self.adj_matrix = [[0] * num_vertices for _ in range(num_vertices)]
        self.adj_list = defaultdict(list)
        self.num_vertices = num_vertices

    def add_edge(self, i, j, weight):
        self.adj_matrix[i][j] = weight
        self.adj_matrix[j][i] = weight
        self.adj_list[i].append((j, weight))
        self.adj_list[j].append((i, weight))

    def dfs(self, start):
        visited = [False] * self.num_vertices
        self._dfs(start, visited)

    def _dfs(self, u, visited):
        visited[u] = True
        print(u, end=' ')
        for v, weight in self.adj_list[u]:
            if not visited[v]:
                self._dfs(v, visited)

5. 图的广度遍历算法

from queue import Queue

class Graph:
    def __init__(self, num_vertices):
        self.adj_matrix = [[0] * num_vertices for _ in range(num_vertices)]
        self.adj_list = defaultdict(list)
        self.num_vertices = num_vertices

    def add_edge(self, i, j, weight):
        self.adj_matrix[i][j] = weight
        self.adj_matrix[j][i] = weight
        self.adj_list[i].append((j, weight))
        self.adj_list[j].append((i, weight))

    def bfs(self, start):
        visited = [False] * self.num_vertices
        q = Queue()
        q.put(start)
        visited[start] = True
        while not q.empty():
            u = q.get()
            print(u, end=' ')
            for v, weight in self.adj_list[u]:
                if not visited[v]:
                    visited[v] = True
                    q.put(v)

您可以根据需要调用这些方法来实现您的需求。