LeetCode 72. Edit Distance

本文介绍了一种计算两个字符串之间的编辑距离的方法,通过插入、删除或替换字符的操作来衡量字符串相似度。采用动态规划的方式实现了一个具体的例子,并给出了C++代码实现。

Given two words word1 and word2, find the minimum number of steps required to convert word1 to word2. (each operation is counted as 1 step.)

You have the following 3 operations permitted on a word:

a) Insert a character

b) Delete a character

c) Replace a character


As indicated in the question. There are three ways to adjust two strings.

We can adjust the words either bottom up or top down.


1: Bottom Up: dp[i][j] = min(min(dp[i-1][j], dp[i][j-1])), dp[i-1][j-1]) + 1; 

dp[i-1][j-1] is to replace ith character to jth character. dp[i-1][j] is to delete ith character, dp[i][j-1] is to insert jth character.


#include <vector>
#include <string>
#include <iostream>
using namespace std;

int minDistance(string word1, string word2) {
    int m = word1.size();
    int n = word2.size();
    vector< vector<int> > dis(m + 1, vector<int>(n + 1, 0));
    for(int i = 0; i <= m; ++i) {
        dis[i][0] = i;
    }

    for(int i = 0; i <= n; ++i) {
        dis[0][i] = i;
    }

    for(int i = 1; i <= m; ++i) {
        for(int j = 1; j <= n; ++j) {
            if(word1[i-1] == word2[j-1]) {
                dis[i][j] = dis[i-1][j-1];
            } else {
                // in this case word[i-1] != word[j-1];
                // in this case, we have three choices. 1: delete, 2: insert, 3: replace.
                // dis[i-1][j-1] : replace
                // dis[i-1][j] : delete
                // dis[i][j-1] : insert
                dis[i][j] = min(min(dis[i-1][j], dis[i][j-1]), dis[i-1][j-1]) + 1;
            }
        }
    }
    return dis[m][n];
}

int main(void) {
    string word1 = "ab";
    string word2 = "abcd";
    int distance = minDistance(word1, word2);
    cout << distance << endl;
}

I honestly dont think I understand this question every well.... even though there is no problem with coding it out. It is better to practise multiple solutions!

【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)内容概要:本文提出了一种基于融合鱼鹰和柯西变异的麻雀优化算法(OCSSA)优化变分模态分解(VMD)参数,并结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)的轴承故障诊断模型。该方法利用西储大学公开的轴承数据集进行验证,通过OCSSA算法优化VMD的分解层数K和惩罚因子α,有效提升信号分解精度,抑制模态混叠;随后利用CNN提取故障特征的空间信息,BiLSTM捕捉时间序列的动态特征,最终实现高精度的轴承故障分类。整个诊断流程充分结合了信号预处理、智能优化与深度学习的优势,显著提升了复杂工况下轴承故障诊断的准确性与鲁棒性。; 适合人群:具备一定信号处理、机器学习及MATLAB编程基础的研究生、科研人员及从事工业设备故障诊断的工程技术人员。; 使用场景及目标:①应用于旋转机械设备的智能运维与故障预警系统;②为轴承等关键部件的早期故障识别提供高精度诊断方案;③推动智能优化算法与深度学习在工业信号处理领域的融合研究。; 阅读建议:建议读者结合MATLAB代码实现,深入理解OCSSA优化机制、VMD参数选择策略以及CNN-BiLSTM网络结构的设计逻辑,通过复现实验掌握完整诊断流程,并可进一步尝试迁移至其他设备的故障诊断任务中进行验证与优化。
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