1021. Deepest Root (25)

给定一个连通且无环的图(可以视为树),任务是找到使得树高度最大的根节点,这样的根节点称为最深根。输入包含一个测试用例,首先给出节点数N(<=10000),接着N-1行描述每条边连接的两个节点。输出所有最深根节点,若不唯一,则按节点编号升序排列。若图不是一棵树,则输出错误信息及连通组件数量。

1021. Deepest Root (25)

时间限制
1500 ms
内存限制
65536 kB
代码长度限制
16000 B
判题程序
Standard
作者
CHEN, Yue

A graph which is connected and acyclic can be considered a tree. The height of the tree depends on the selected root. Now you are supposed to find the root that results in a highest tree. Such a root is called the deepest root.

Input Specification:

Each input file contains one test case. For each case, the first line contains a positive integer N (<=10000) which is the number of nodes, and hence the nodes are numbered from 1 to N. Then N-1 lines follow, each describes an edge by given the two adjacent nodes' numbers.

Output Specification:

For each test case, print each of the deepest roots in a line. If such a root is not unique, print them in increasing order of their numbers. In case that the given graph is not a tree, print "Error: K components" where K is the number of connected components in the graph.

Sample Input 1:
5
1 2
1 3
1 4
2 5
Sample Output 1:
3
4
5
Sample Input 2:
5
1 3
1 4
2 5
3 4
Sample Output 2:
Error: 2 components
使用dfs便可以判断树的个数,如果个数不为1,则按要求输出结果即可;如果个数为1,则需要给出树中有对大深度的线路上的根节点。采取的方法是,第一步,任取一个节点,从这个节点开始,进行dfs,得到深度最大的线路对应的根节点,这些根节点一定是最终所要输出的集合中的部分或者全部,具体可以画一棵树来进行分析。第二步,从第一步得到的根节点中,任选一个再进行dfs,得到一个根节点的集合。将第一步和第二步中得到的集合取并集,然后按升序输出即可。之所以这里使用集合来描述,是为了避免第一步和第二步中得到的两个集合中有相同的元素,使用别的数据结构来进行存储的时候,需要注意这种情况。
#include <iostream>
#include <map>
#include <vector>
#include <set>
#include <cstdio>
#include <algorithm>
using namespace std;
map <int, vector<int> > tree;
vector<int> visited;
set<int> furthest_node;
int max_dis=0;
void dfs(int node,int dis);
int main()
{

    int num_node;
    cin >>num_node;
    for(int i=1;i<num_node;i++)
    {
        int a,b;
        cin >>a >>b;
        tree[a].push_back(b);
        tree[b].push_back(a);
        visited.push_back(0);
    }
    for(int i=0;i<=num_node+1;i++)
        visited.push_back(0);
    int k=0;
    for(int i=1;i<=num_node;i++)
    {
        if(visited[i]!=1)
        {
            visited[i]=1;
            furthest_node.insert(i);//需要加入,防止输入仅有一个节点的情况的出现;
            dfs(i,0);
            k++;
        }
    }
    for(int i=0;i<=num_node+1;i++)
        visited[i]=0;
    max_dis=0;
    set<int> record=furthest_node;
    if(k==1)
    {
        int a=*furthest_node.begin();
        visited[a]=1;
        dfs(a,0);
        set<int>::iterator iter=record.begin();
        for(;iter!=record.end();iter++)
            furthest_node.insert(*iter);
        for(iter=furthest_node.begin();iter!=furthest_node.end();iter++)
           cout <<*iter<<endl;
    }
    else
        cout <<"Error: "<<k<<" components"<<endl;

    return 0;
}
void dfs(int node,int dis)
{
    vector<int>::iterator iter=tree[node].begin();
    for(;iter!=tree[node].end();iter++)
    {
        int tmp=*iter;
        if(visited[tmp]!=1)
        {
            if(dis+1>max_dis)
            {
                max_dis=dis+1;
                furthest_node.clear();
                furthest_node.insert(tmp);
            }
            else if(dis+1==max_dis)
                furthest_node.insert(tmp);
            visited[tmp]=1;
            dfs(tmp,dis+1);
        }
    }
}


在你的代码中,删除最后一个节点时可能会出现问题。这是因为你在处理 `last_node` 和 `last_parent` 的逻辑时没有完全考虑所有边界情况,尤其是当目标节点本身就是树的唯一节点时。 以下是对代码的改进版本,并附有详细解释: --- ### 改进后的代码 ```python from collections import deque class BTreeNode: def __init__(self, val): self.val = val self.left = None self.right = None class BinarySearchTree: def __init__(self): self.root = None def search(self, val): """ Search for a value in the tree and return the node containing it. @param val: The value to search for. @return: The node containing the value, or None if not found. """ current = self.root while current: if val == current.val: return current elif val < current.val: current = current.left else: current = current.right return None def delete(self, val): """ Delete a value from the tree if it is present. To delete a non-leaf node, replace it with the deepest node (last node in level order traversal). @param val: The value to delete. @return: True if deletion was successful, False otherwise. """ target = self.search(val) if not target: return False # Value not found in the tree # Special case: If the tree has only one node (root), delete it directly if self.root == target and not self.root.left and not self.root.right: self.root = None return True # Find the deepest node (last node in level order traversal) and its parent queue = deque([self.root]) last_node = None last_parent = None while queue: last_parent = last_node last_node = queue.popleft() if last_node.left: queue.append(last_node.left) if last_node.right: queue.append(last_node.right) # If the target node is the deepest node itself, handle this case separately if target == last_node: if last_parent: if last_parent.left == last_node: last_parent.left = None else: last_parent.right = None else: self.root = None # Tree becomes empty return True # Replace the target node's value with the deepest node's value target.val = last_node.val # Remove the deepest node from the tree if last_parent.left == last_node: last_parent.left = None else: last_parent.right = None return True ``` --- ### 给出解释 1. **`search` 方法**: - 用于查找目标值 `val` 是否存在于树中。 - 如果找到目标值,则返回包含该值的节点;否则返回 `None`。 2. **`delete` 方法的核心逻辑**: - 首先通过 `search` 方法找到目标节点 `target`。如果目标值不存在,则直接返回 `False`。 - 如果树只有一个节点(即根节点),并且该节点就是要删除的目标节点,则直接将 `self.root` 设置为 `None`。 - 使用广度优先搜索(BFS)找到树中最深的节点(即最后一个节点)及其父节点。 - 如果目标节点本身是最深的节点,则直接将其从树中移除。 - 否则,用最深节点的值替换目标节点的值,并将最深节点从树中移除。 3. **关键点**: - 在删除非叶子节点时,用最深节点的值替换目标节点的值,以保持二叉搜索树的性质。 - 处理边界情况:当目标节点是最深节点时,需要正确更新其父节点的子节点引用。 --- ### 测试代码 以下是一个简单的测试用例,展示如何使用 `BinarySearchTree` 类: ```python # Test the BinarySearchTree implementation bst = BinarySearchTree() # Insert values into the tree values_to_insert = [10, 5, 15, 3, 7, 12, 18] for value in values_to_insert: bst.insert(value) # Helper function to print the tree in-order def in_order_traversal(node): if node is None: return in_order_traversal(node.left) print(node.val, end=" ") in_order_traversal(node.right) # Print the tree in-order in_order_traversal(bst.root) # Output: 3 5 7 10 12 15 18 print() # Delete a leaf node bst.delete(3) in_order_traversal(bst.root) # Output: 5 7 10 12 15 18 print() # Delete a non-leaf node bst.delete(10) in_order_traversal(bst.root) # Output: 5 7 12 15 18 print() # Delete the root node bst.delete(15) in_order_traversal(bst.root) # Output: 5 7 12 18 print() # Delete the last node bst.delete(18) bst.delete(12) bst.delete(7) bst.delete(5) in_order_traversal(bst.root) # Output: (empty) ``` --- ###
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