Leetcode: Happy Number

快乐数判断算法
本文介绍了一种用于判断正整数是否为快乐数的算法。快乐数是指通过将该数替换为其各位数字平方之和并重复此过程,最终能得到1的正整数。文章详细讨论了避免无限循环的方法,并给出了具体的实现代码。
Write an algorithm to determine if a number is "happy".

A happy number is a number defined by the following process: Starting with any positive integer, replace the number by the sum of the squares of its digits, and repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. Those numbers for which this process ends in 1 are happy numbers.

Example: 19 is a happy number

1^2 + 9^2 = 82
8^2 + 2^2 = 68
6^2 + 8^2 = 100
1^2 + 0^2 + 0^2 = 1

First Time: Not good, when I try to get to the most significant bit, I define current bit called cur, and cur *= 10 each time. This might gives rise to overflow in line 12 when cur * 10. To overcome this I have to define cur to be a long.

The best way to get each digit is to use n%10, and n /=10 each loop. Dividing will avoid unnecessary overflow.

BTW, no need to define an arraylist to store all the digits.

 1 public class Solution {
 2     public boolean isHappy(int n) {
 3         if (n <= 0) return false;
 4         if (n == 1) return true;
 5         HashSet<Integer> set = new HashSet<Integer>();
 6         while(!set.contains(n)) {
 7             set.add(n);
 8             long cur = 1;
 9             ArrayList<Integer> digits = new ArrayList<Integer>();
10             int sum = 0;
11             while (n / cur >= 1) {
12                 digits.add((int)(n % (cur*10) / cur));
13                 cur *= 10;
14             }
15             for (int digit : digits) {
16                 sum += (int)Math.pow(digit, 2);
17             }
18             n = sum;
19             if (n == 1) return true;
20         }
21         return false;
22     }
23 }

About syntax, line 16 it is correct to write it as: sum += Math.pow(digit, 2),

but it is incorrect as: sum = sum + Math.pow(digit, 2), should write as sum = sum + (int)Math.pow(digit, 2)

So better cast pow into integer before use it.

 

Second Time: Better

The best way to get each digit is to use n%10, and n /=10 each loop. 

 1 public class Solution {
 2     public boolean isHappy(int n) {
 3         if (n < 0) return false;
 4         if (n == 1) return true;
 5         HashSet<Integer> set = new HashSet<Integer>();
 6         while (n != 1) {
 7             if (set.contains(n)) return false;
 8             set.add(n);
 9             int sum = 0;
10             while (n > 0) {
11                 sum += (int)Math.pow(n%10, 2);
12                 n /= 10;
13             }
14             n = sum;
15         }
16         return true;
17     }
18 }

 

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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