2.设计一个基于图的邻接表的图的基本操作的演示程序。（234） 实现下列四种基本运算：（1）创建图；（2）深度优先遍历图；（3）广度优先遍历图；（4）转换图的存储结构()

设计一个简单的基于邻接表的图数据结构及其基本操作的Python示例：

class Node:
    def __init__(self, value):
        self.value = value
        self.neighbors = []

class Graph:
    def __init__(self):
        self.nodes = {}

    # 创建图 - 添加顶点和边
    def add_vertex(self, value):
        if value not in self.nodes:
            self.nodes[value] = Node(value)

    def add_edge(self, src, dest):
        if src in self.nodes and dest in self.nodes:
            self.nodes[src].neighbors.append(dest)
            self.nodes[dest].neighbors.append(src)  # 如果图是非双向的，这一步可以省略

    # 深度优先搜索 (DFS)
    def dfs(self, start_node):
        visited = set()
        stack = [start_node]

        while stack:
            node = stack.pop()
            if node not in visited:
                visited.add(node)
                print(node.value, end=" -> ")
                for neighbor in node.neighbors:
                    stack.append(neighbor)

    # 广度优先搜索 (BFS)
    def bfs(self, start_node):
        visited = set()
        queue = [start_node]

        while queue:
            node = queue.pop(0)
            if node not in visited:
                visited.add(node)
                print(node.value, end=" -> ")
                for neighbor in node.neighbors:
                    queue.append(neighbor)

    # 转换存储结构 - 将邻接列表转邻接矩阵 (仅适用于无向图)
    def to_adjacency_matrix(self):
        matrix = [[0] * len(self.nodes) for _ in range(len(self.nodes))]
        
        for node, neighbors in self.nodes.items():
            for neighbor in neighbors.neighbors:
                matrix[node][neighbor] = 1
        
        return matrix

# 示例
graph = Graph()
graph.add_vertex("A")
graph.add_vertex("B")
graph.add_vertex("C")
graph.add_vertex("D")
graph.add_edge("A", "B")
graph.add_edge("A", "C")
graph.add_edge("B", "C")
graph.add_edge("C", "D")

print("\nDepth First Search:")
graph.dfs(graph.nodes["A"])

print("\nBreadth First Search:")
graph.bfs(graph.nodes["A"])

print("\nAdjacency Matrix:")
matrix = graph.to_adjacency_matrix()
for row in matrix:
    print(row)

这个程序首先定义了节点和图的数据结构，然后实现了添加顶点、添加边、深度优先搜索和广度优先搜索的操作，最后展示了如何将邻接表转换成邻接矩阵。