


Detailed explanation of several recursive total permutation algorithms in JavaScript
Exchange (recursive)
<html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>Full Permutation(Recursive Swap) - Mengliao Software</title> </head> <body> <p>Full Permutation(Recursive Swap)<br /> Mengliao Software Studio - Bosun Network Co., Ltd.<br /> 2011.05.24</p> <script type="text/javascript"> /* 全排列(递归交换)算法 1、将第一个位置分别放置各个不同的元素; 2、对剩余的位置进行全排列(递归); 3、递归出口为只对一个元素进行全排列。 */ function swap(arr,i,j) { if(i!=j) { var temp=arr[i]; arr[i]=arr[j]; arr[j]=temp; } } var count=0; function show(arr) { document.write("P<sub>"+ ++count+"</sub>: "+arr+"<br />"); } function perm(arr) { (function fn(n) { //为第n个位置选择元素 for(var i=n;i<arr.length;i++) { swap(arr,i,n); if(n+1<arr.length-1) //判断数组中剩余的待全排列的元素是否大于1个 fn(n+1); //从第n+1个下标进行全排列 else show(arr); //显示一组结果 swap(arr,i,n); } })(0); } perm(["e1","e2","e3","e4"]); </script> </body> </html>
Link (recursive)
<html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>Full Permutation(Recursive Link) - Mengliao Software</title> </head> <body> <p>Full Permutation(Recursive Link)<br /> Mengliao Software Studio - Bosun Network Co., Ltd.<br /> 2012.03.29</p> <script type="text/javascript"> /* 全排列(递归链接)算法 1、设定源数组为输入数组,结果数组存放排列结果(初始化为空数组); 2、逐一将源数组的每个元素链接到结果数组中(生成新数组对象); 3、从原数组中删除被链接的元素(生成新数组对象); 4、将新的源数组和结果数组作为参数递归调用步骤2、3,直到源数组为空,则输出一个排列。 */ var count=0; function show(arr) { document.write("P<sub>"+ ++count+"</sub>: "+arr+"<br />"); } function perm(arr) { (function fn(source, result) { if (source.length == 0) show(result); else for (var i = 0; i < source.length; i++) fn(source.slice(0, i).concat(source.slice(i + 1)), result.concat(source[i])); })(arr, []); } perm(["e1", "e2", "e3", "e4"]); </script> </body> </html>
Backtracking (recursive)
<html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>Full Permutation(Recursive Backtrack) - Mengliao Software</title> </head> <body> <p>Full Permutation(Recursive Backtrack)<br /> Mengliao Software Studio - Bosun Network Co., Ltd.<br /> 2012.03.29</p> <script type="text/javascript"> /* 全排列(递归回溯)算法 1、建立位置数组,即对位置进行排列,排列成功后转换为元素的排列; 2、建立递归函数,用来搜索第n个位置; 3、第n个位置搜索方式与八皇后问题类似。 */ var count = 0; function show(arr) { document.write("P<sub>" + ++count + "</sub>: " + arr + "<br />"); } function seek(index, n) { if (n >= 0) //判断是否已回溯到了第一个位置之前,即已经找到了所有位置排列 if (index[n] < index.length - 1) { //还有下一个位置可选 index[n]++; //选择下一个位置 if ((function () { //该匿名函数判断该位置是否已经被选择过 for (var i = 0; i < n; i++) if (index[i] == index[n]) return true; //已选择 return false; //未选择 })()) return seek(index, n); //重新找位置 else return true; //找到 } else { //当前无位置可选,进行递归回溯 index[n] = -1; //取消当前位置 if (seek(index, n - 1)) //继续找上一个位置 return seek(index, n); //重新找当前位置 else return false; //已无位置可选 } else return false; } function perm(arr) { var index = new Array(arr.length); for (var i = 0; i < index.length; i++) index[i] = -1; //初始化所有位置为-1,以便++后为0 for (i = 0; i < index.length - 1; i++) seek(index, i); //先搜索前n-1个位置 while (seek(index, index.length - 1)) { //不断搜索第n个位置,即找到所有位置排列 var temp = []; for (i = 0; i < index.length; i++) //将位置之转换为元素 temp.push(arr[index[i]]); show(temp); } } perm(["e1", "e2", "e3", "e4"]); </script> </body> </html>
The above is the detailed content of Detailed explanation of several recursive total permutation algorithms in JavaScript. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



Written above & the author’s personal understanding: At present, in the entire autonomous driving system, the perception module plays a vital role. The autonomous vehicle driving on the road can only obtain accurate perception results through the perception module. The downstream regulation and control module in the autonomous driving system makes timely and correct judgments and behavioral decisions. Currently, cars with autonomous driving functions are usually equipped with a variety of data information sensors including surround-view camera sensors, lidar sensors, and millimeter-wave radar sensors to collect information in different modalities to achieve accurate perception tasks. The BEV perception algorithm based on pure vision is favored by the industry because of its low hardware cost and easy deployment, and its output results can be easily applied to various downstream tasks.

Common challenges faced by machine learning algorithms in C++ include memory management, multi-threading, performance optimization, and maintainability. Solutions include using smart pointers, modern threading libraries, SIMD instructions and third-party libraries, as well as following coding style guidelines and using automation tools. Practical cases show how to use the Eigen library to implement linear regression algorithms, effectively manage memory and use high-performance matrix operations.

Face detection and recognition technology is already a relatively mature and widely used technology. Currently, the most widely used Internet application language is JS. Implementing face detection and recognition on the Web front-end has advantages and disadvantages compared to back-end face recognition. Advantages include reducing network interaction and real-time recognition, which greatly shortens user waiting time and improves user experience; disadvantages include: being limited by model size, the accuracy is also limited. How to use js to implement face detection on the web? In order to implement face recognition on the Web, you need to be familiar with related programming languages and technologies, such as JavaScript, HTML, CSS, WebRTC, etc. At the same time, you also need to master relevant computer vision and artificial intelligence technologies. It is worth noting that due to the design of the Web side

The bottom layer of the C++sort function uses merge sort, its complexity is O(nlogn), and provides different sorting algorithm choices, including quick sort, heap sort and stable sort.

The convergence of artificial intelligence (AI) and law enforcement opens up new possibilities for crime prevention and detection. The predictive capabilities of artificial intelligence are widely used in systems such as CrimeGPT (Crime Prediction Technology) to predict criminal activities. This article explores the potential of artificial intelligence in crime prediction, its current applications, the challenges it faces, and the possible ethical implications of the technology. Artificial Intelligence and Crime Prediction: The Basics CrimeGPT uses machine learning algorithms to analyze large data sets, identifying patterns that can predict where and when crimes are likely to occur. These data sets include historical crime statistics, demographic information, economic indicators, weather patterns, and more. By identifying trends that human analysts might miss, artificial intelligence can empower law enforcement agencies

01 Outlook Summary Currently, it is difficult to achieve an appropriate balance between detection efficiency and detection results. We have developed an enhanced YOLOv5 algorithm for target detection in high-resolution optical remote sensing images, using multi-layer feature pyramids, multi-detection head strategies and hybrid attention modules to improve the effect of the target detection network in optical remote sensing images. According to the SIMD data set, the mAP of the new algorithm is 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving a better balance between detection results and speed. 02 Background & Motivation With the rapid development of remote sensing technology, high-resolution optical remote sensing images have been used to describe many objects on the earth’s surface, including aircraft, cars, buildings, etc. Object detection in the interpretation of remote sensing images

1. Background of the Construction of 58 Portraits Platform First of all, I would like to share with you the background of the construction of the 58 Portrait Platform. 1. The traditional thinking of the traditional profiling platform is no longer enough. Building a user profiling platform relies on data warehouse modeling capabilities to integrate data from multiple business lines to build accurate user portraits; it also requires data mining to understand user behavior, interests and needs, and provide algorithms. side capabilities; finally, it also needs to have data platform capabilities to efficiently store, query and share user profile data and provide profile services. The main difference between a self-built business profiling platform and a middle-office profiling platform is that the self-built profiling platform serves a single business line and can be customized on demand; the mid-office platform serves multiple business lines, has complex modeling, and provides more general capabilities. 2.58 User portraits of the background of Zhongtai portrait construction

Written above & The author’s personal understanding is that in the autonomous driving system, the perception task is a crucial component of the entire autonomous driving system. The main goal of the perception task is to enable autonomous vehicles to understand and perceive surrounding environmental elements, such as vehicles driving on the road, pedestrians on the roadside, obstacles encountered during driving, traffic signs on the road, etc., thereby helping downstream modules Make correct and reasonable decisions and actions. A vehicle with self-driving capabilities is usually equipped with different types of information collection sensors, such as surround-view camera sensors, lidar sensors, millimeter-wave radar sensors, etc., to ensure that the self-driving vehicle can accurately perceive and understand surrounding environment elements. , enabling autonomous vehicles to make correct decisions during autonomous driving. Head
