This approach involves iterating through each bit of the integer by continuously shifting the bits to the right and checking the least significant bit. Whenever the least significant bit is set, we increment our count. This continues until all bits have been checked.
Time Complexity: O(b) where b is the number of bits in the integer.
Space Complexity: O(1) as no additional space is required.
1function hammingWeight(n) {
2 let count = 0;
3 while (n !== 0) {
4 count += n & 1;
5 n = n >>> 1; // Unsigned right shift
6 }
7 return count;
8}
9
10console.log(hammingWeight(11)); // Output: 3
11console.log(hammingWeight(128)); // Output: 1
12console.log(hammingWeight(2147483645)); // Output: 30The JavaScript function computes the count of set bits in an integer using the bitwise AND to identify set bits and the `>>>` unsigned right shift to iterate through each bit position.
This approach leverages the property of bit manipulation where the operation n & (n - 1) results in the number with the lowest set bit turned off. By performing this operation iteratively, the loop proceeds directly to the next set bit, thus reducing the number of iterations required compared to shifting each bit position.
Time Complexity: O(k) where k is the number of set bits.
Space Complexity: O(1) as no extra space is required.
1#include <iostream>
2using namespace std;
3
4int hammingWeight(uint32_t n) {
5 int count = 0;
6 while (n) {
7 n &= (n - 1);
8 count++;
9 }
10 return count;
11}
12
13int main() {
14 cout << hammingWeight(11) << endl; // Output: 3
15 cout << hammingWeight(128) << endl; // Output: 1
16 cout << hammingWeight(2147483645) << endl; // Output: 30
17 return 0;
18}This C++ solution optimizes the counting of set bits by iteratively turning off the lowest set bit until the integer becomes zero, counting the number of operations performed.