用编程语言实现Kruskal算法和P算法求最小生成树（要写通用算法）

Kruskal算法和Prim算法都是用于寻找无向图中最小生成树的经典算法。以下是通用的Kruskal算法和Prim算法的Python实现：

**Kruskal算法 (适用于边集形式)**:

def kruskal(graph):
    edges = sorted(graph.items(), key=lambda x: x[1])  # 按权值升序排列边
    mst = []
    graph_set = set()

    for edge in edges:
        u, v, weight = edge
        if u not in graph_set and v not in graph_set:  # 避免形成环
            graph_set.add(u)
            graph_set.add(v)
            mst.append(edge)

    return mst

# 示例：
graph = {('A', 'B'): 4, ('A', 'D'): 8, ('B', 'C'): 5, ('C', 'D'): 9, ('D', 'E'): 7}
mst = kruskal(graph)

**Prim算法 (适用于邻接矩阵或邻接表)**

import heapq

def prim(graph, start=None):  # 如果未指定起始节点，默认选择任意节点
    if start is None:
        start, _ = min((node, weight) for node, weight in enumerate(graph[start] if isinstance(start, str) else graph))
    visited = set()
    unvisited = [(0, start)]
    minheap = []

    while unvisited:
        current_weight, current_node = heapq.heappop(minheap)
        if current_node in visited:
            continue
        visited.add(current_node)

        for neighbor, weight in graph[current_node].items():
            if neighbor not in visited:
                heapq.heappush(minheap, (weight, neighbor))

    return [edge for _, edge in visited.items()]

# 示例：
if isinstance(graph, dict):  # 使用邻接字典表示邻接矩阵
    matrix_graph = [[0 if 'A' not in node or 'B' not in node else weight for weight in row] for node, row in graph.items()]
else:  # 使用邻接列表表示邻接表
    matrix_graph = ...  # 自行填充邻接表结构
mst = prim(matrix_graph, 'A')