LintCode 1561: BST Node Distance (BST 经典题)

原题如下:
1561. BST Node Distance
Given a list of numbers, construct a BST from it(you need to insert nodes one-by-one with the given order to get the BST) and find the distance between two given nodes.

Example
input:
numbers = [2,1,3]
node1 = 1
node2 = 3
output:
2
Notice
If two nodes do not appear in the BST, return -1
We guarantee that there are no duplicate nodes in BST
The node distance means the number of edges between two nodes

这题主要考BST的操作(包括BST的按输入顺序构造, 找LCA, 算节点间距离,等等)。

解法1:
先按输入构造BST,然后找两个节点的LCA, 再把两个节点到LCA的距离加起来即可。

struct Node {
    Node * left;
    Node * right;
    int val;
    Node(int v) : val(v), left(NULL), right(NULL) {}
};


class Solution {
public:
    /**
     * @param numbers: the given list
     * @param node1: the given node1
     * @param node2: the given node2
     * @return: the distance between two nodes
     */
    int bstDistance(vector<int> &numbers, int node1, int node2) {
        int len = numbers.size();
        if (len <= 1) return -1;
        if (find(numbers.begin(), numbers.end(), node1) == numbers.end() ||
            find(numbers.begin(), numbers.end(), node2) == numbers.end()) 
            return -1;
        
        Node * root = new Node(numbers[0]);
        for (int i = 1; i < len; ++i) {
            constructBst(root, numbers[i]);
        }

        return findDist(root, node1, node2);
    }
    
private:
    void constructBst(Node * root, int n) {

        if (n > root->val) {
            if (!root->right) {
                root->right = new Node(n);
            } else {
                constructBst(root->right, n);
            }
        } else {
            if (!root->left) {
                root->left = new Node(n);
            } else {
                constructBst(root->left, n);
            }
        }
    }
    
    Node * findLCA(Node * root, int a, int b) {
        if (!root) return NULL;
        
        if ((a > root->val) && (b > root->val)) {
            return findLCA(root->right, a, b);
        } else if ((a < root->val) && (b < root->val)) {
            return findLCA(root->left, a, b);
        } else {
            return root;   
        }
    }
    
    int findDist(Node * root, int a, int b) {
        Node * LCA = findLCA(root, a, b);
        if (!LCA) return -1;
        return findNumOfLevels(LCA, a) + findNumOfLevels(LCA, b);
    }
    
    int findNumOfLevels(Node * root, int n) {
        if (!root) return -1;
        if (root->val == n) return 0;
        if (n > root->val) return findNumOfLevels(root->right, n) + 1;
        return findNumOfLevels(root->left, n) + 1;
    }
};
# 精读数组/链表/树/图,手写经典算法 ## 详解 数组、链表、树、图是计算机科学中最基础且最重要的**数据结构**,它们构成了大多数算法和系统实现的基础。掌握这些结构及其相关经典算法对于深入理解编程和算法设计至关重要。 --- ## 一、数组(Array) ### 1. 定义与特点 - **连续内存**:元素在内存中连续存储。 - **随机访问**:支持通过索引快速访问 $O(1)$。 - **插入/删除效率低**:平均时间复杂度 $O(n)$。 ### 2. 经典算法 #### (1)两数之和(Two Sum) ```python def two_sum(nums, target): seen = {} for i, num in enumerate(nums): complement = target - num if complement in seen: return [seen[complement], i] seen[num] = i return [] ``` #### (2)最长连续递增序列 ```python def find_length_of_lcis(nums): max_len = count = 1 for i in range(1, len(nums)): if nums[i] > nums[i - 1]: count += 1 max_len = max(max_len, count) else: count = 1 return max_len ``` --- ## 二、链表(Linked List) ### 1. 定义与特点 - **非连续存储**:每个节点包含数据和指向下一个节点的指针。 - **插入/删除高效**:时间复杂度 $O(1)$(已知位置)。 - **访问效率低**:需从头遍历,时间复杂度 $O(n)$。 ### 2. 经典算法 #### (1)反转链表(Iterative) ```python class ListNode: def __init__(self, val=0, next=None): self.val = val self.next = next def reverse_list(head): prev = None curr = head while curr: next_temp = curr.next curr.next = prev prev = curr curr = next_temp return prev ``` #### (2)判断链表是否有环(Floyd判圈算法) ```python def has_cycle(head): slow = fast = head while fast and fast.next: slow = slow.next fast = fast.next.next if slow == fast: return True return False ``` --- ## 三、树(Tree) ### 1. 定义与特点 - **非线性结构**:每个节点有多个子节点。 - **典型应用**:二叉搜索树(BST)、堆、平衡树(AVL、红黑树)等。 ### 2. 经典算法 #### (1)前序遍历(递归) ```python def preorder_traversal(root): result = [] def dfs(node): if not node: return result.append(node.val) dfs(node.left) dfs(node.right) dfs(root) return result ``` #### (2)判断是否为二叉搜索树(BST) ```python def is_valid_bst(root): def dfs(node, low=float('-inf'), high=float('inf')): if not node: return True if not (low < node.val < high): return False return dfs(node.left, low, node.val) and dfs(node.right, node.val, high) return dfs(root) ``` --- ## 四、图(Graph) ### 1. 定义与特点 - **顶点与边的集合**:用于表示对象之间的关系。 - **常见表示方式**:邻接矩阵、邻接表。 ### 2. 经典算法 #### (1)深度优先搜索(DFS) ```python def dfs(graph, start): visited = set() def helper(node): visited.add(node) for neighbor in graph[node]: if neighbor not in visited: helper(neighbor) helper(start) return visited ``` #### (2)广度优先搜索(BFS) ```python from collections import deque def bfs(graph, start): visited = set() queue = deque([start]) while queue: node = queue.popleft() if node not in visited: visited.add(node) queue.extend(graph[node] - visited) return visited ``` #### (3)Dijkstra最短路径算法 ```python import heapq def dijkstra(graph, start): distances = {node: float('inf') for node in graph} distances[start] = 0 heap = [(0, start)] while heap: current_dist, u = heapq.heappop(heap) if current_dist > distances[u]: continue for v, weight in graph[u].items(): distance = current_dist + weight if distance < distances[v]: distances[v] = distance heapq.heappush(heap, (distance, v)) return distances ``` --- ## 知识点 1. **数组操作**:包括查找、排序、双指针技巧等,适用于线性结构处理。 2. **链表结构**:掌握节点操作、反转、判环等基础链表算法。 3. **树与图遍历**:掌握DFS、BFS、递归遍历与最短路径等核心算法。
最新发布
09-14
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