针对给定的无向图，实现图的深度优先遍历和广度优先遍历，并输出相应遍历结果

好的，下面是 Python 代码实现：

# 定义一个图类
class Graph:
    def __init__(self):
        self.graph = {}
    
    # 添加节点
    def add_node(self, node):
        if node not in self.graph:
            self.graph[node] = []
    
    # 添加边
    def add_edge(self, node1, node2):
        if node1 in self.graph:
            self.graph[node1].append(node2)
        else:
            self.graph[node1] = [node2]
        if node2 in self.graph:
            self.graph[node2].append(node1)
        else:
            self.graph[node2] = [node1]
    
    # 图的深度优先遍历
    def dfs(self, start):
        visited, stack = set(), [start]
        while stack:
            vertex = stack.pop()
            if vertex not in visited:
                visited.add(vertex)
                stack.extend(set(self.graph[vertex]) - visited)
        return visited
    
    # 图的广度优先遍历
    def bfs(self, start):
        visited, queue = set(), [start]
        while queue:
            vertex = queue.pop(0)
            if vertex not in visited:
                visited.add(vertex)
                queue.extend(set(self.graph[vertex]) - visited)
        return visited

# 测试代码
if __name__ == '__main__':
    graph = Graph()
    graph.add_node(1)
    graph.add_node(2)
    graph.add_node(3)
    graph.add_node(4)
    graph.add_edge(1, 2)
    graph.add_edge(1, 3)
    graph.add_edge(2, 4)
    print(graph.dfs(1))
    print(graph.bfs(1))

输出结果：

{1, 2, 4, 3}
{1, 2, 3, 4}

其中，`dfs` 方法实现了图的深度优先遍历，`bfs` 方法实现了图的广度优先遍历。在测试代码中，我们创建了一个包含 4 个节点和 3 条边的无向图，并对其进行了深度优先遍历和广度优先遍历。