leetcode 191. Number of 1 Bits

本文介绍了如何通过编程方法计算一个整数中二进制表示中1的数量,即Hamming重量。通过逐步分析,提供了一个简洁高效的算法实现,并附有示例代码。

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题目

Write a function that takes an unsigned integer and returns the number of ’1’ bits it has (also known as the Hamming weight).

For example, the 32-bit integer ’11’ has binary representation 00000000000000000000000000001011, so the function should return 3.

分析

1,把一个整数减去1,都是把最右边的1变成0.如果它的右边还有0,的话,所有的0变成1,而它左边所有位都保持不变。
2,接下来我们把一个整数和它减去1的结果做位运算,相当于把它最右边的1变成0,。还是以前面的1100为例,它减去1的结果是1011。我们再把1100和1011做位与运算,得到的结果是1000。我们把1100最右边的1变成了0,结果刚好就是1000。
3,我们把上面的分析结果总结起来就是:把一个整数减去1,再和原整数做与运算,会把该整数最右边的一个1变成0。那么一个整数的二进制表示中有多少个1,就可以进行多少次这样的操作。
4,基于这种思路,我们可以写出新的代码(如下所示)。

public class Solution {
    // you need to treat n as an unsigned value
    public int hammingWeight(int n) {

        int count = 0;
        while(n != 0){
            n = n & (n-1);
            count++;
        }//while
        return count;
    }
}

参考链接

参考链接1:
http://blog.163.com/kevinlee_2010/blog/static/169820820201541005951696/

### LeetCode Problems Involving Counting the Number of 1s in Binary Representation #### Problem Description from LeetCode 191. Number of 1 Bits A task involves writing a function that receives an unsigned integer and returns the quantity of '1' bits within its binary form. The focus lies on identifying and tallying these specific bit values present in any given input number[^1]. ```python class Solution: def hammingWeight(self, n: int) -> int: count = 0 while n: count += n & 1 n >>= 1 return count ``` This Python code snippet demonstrates how to implement the solution using bitwise operations. #### Problem Description from LeetCode 338. Counting Bits Another related challenge requires generating an output list where each element represents the amount of set bits ('1') found in the binary notation for integers ranging from `0` up to a specified value `n`. This problem emphasizes creating an efficient algorithm capable of handling ranges efficiently[^4]. ```python def countBits(num): result = [0] * (num + 1) for i in range(1, num + 1): result[i] = result[i >> 1] + (i & 1) return result ``` Here, dynamic programming principles are applied alongside bitwise shifts (`>>`) and AND (`&`) operators to optimize performance during computation. #### Explanation Using Brian Kernighan Algorithm For optimizing further especially with large inputs, applying algorithms like **Brian Kernighan** offers significant advantages due to reduced iterations needed per operation compared against straightforward methods iterating through all possible positions or dividing repeatedly until reaching zero. The core idea behind this method relies upon subtracting powers-of-two corresponding only to those places holding actual ‘ones’ thereby skipping over zeroes entirely thus reducing unnecessary checks: ```python def hammingWeight(n): count = 0 while n != 0: n &= (n - 1) count += 1 return count ``` --related questions-- 1. How does the Hamming weight calculation differ between signed versus unsigned integers? 2. Can you explain why shifting right works effectively when determining counts of one-bits? 3. What optimizations exist beyond basic iteration techniques for calculating bit counts? 4. Is there any difference in implementation logic required across various programming languages supporting similar syntaxes? 5. Why might someone choose the Brian Kernighan approach over other strategies?
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