LeetCode 191. Number of 1 Bits

本文介绍了一种高效的方法来计算一个无符号整数中二进制表示形式下1的个数(即汉明重量)。通过使用位操作,特别是按位与运算符,可以在O(k)的时间复杂度内完成此操作,其中k为二进制1的个数。

191. Number of 1 Bits
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.

简而言之就是看一个无符号整数二进制数1的个数

    /* 二进制数借位会从最右边的1来借,如:
    12 = 0000 1100, 12 - 1 = 11 = 0000 1001 
      0000 1100
    & 0000 1001
    = 0000 1000 = 8 (取掉了最右边的1)
    同理:
    8 = 0000 1000, 8 - 1 = 7 = 0000 0111
      0000 1000
    & 0000 0111
    = 0000 0000 = 0
    如此,我们便算出了12的二进制1的个数
    */
    int hammingWeight(uint32_t n) {
        int counter = 0;
        while(n){
            n &= (n - 1);
            ++counter;
        }
        return counter;
    }
### 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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