191. Number of 1 Bits - Leetcode Solution

Leetcode Problem Link Code Solution Link:

💡 Step-by-Step Thought Process

The problem requires counting the number of 1 bits (Hamming weight) in the binary representation of an unsigned integer n.

The solution uses a bitwise operation to efficiently count 1 bits. Here’s how it works:

Initialization: We initialize a counter ans to 0 to track the number of 1 bits.
Bitwise Processing: While n is not zero:
- Increment ans to count a 1 bit.
- Update n to n & (n-1), which clears the rightmost 1 bit in n. For example, if n = 1011, then n-1 = 1010, and n & (n-1) = 1010, removing the rightmost 1.
Result: Return ans, which represents the total number of 1 bits.
Why This Works: The operation n & (n-1) always removes the rightmost 1 bit because n-1 flips the rightmost 1 to 0 and all trailing 0s to 1s, so the AND operation preserves all bits except the rightmost 1. This process repeats exactly as many times as there are 1 bits, ensuring an accurate count.
Time and Space Complexity: The loop runs once per 1 bit in n, which is at most 32 for a 32-bit integer, so the time complexity is O(k), where k is the number of 1 bits (effectively O(1) for fixed-size integers). The space complexity is O(1) as only a single counter is used.

Code Solution

class Solution:
    def hammingWeight(self, n: int) -> int:
        ans = 0

        while n != 0:
            ans += 1
            n = n & (n-1)
        
        return ans
        # Time: O(Bits), Space: O(1)

class Solution {
public:
    int hammingWeight(uint32_t n) {
        int ans = 0;

        while (n != 0) {
            ans++;
            n &= (n - 1);
        }

        return ans;
    }
};

public class Solution {
    public int hammingWeight(int n) {
        int ans = 0;

        while (n != 0) {
            ans++;
            n &= (n - 1);
        }

        return ans;
    }
}

var hammingWeight = function(n) {
    var ans = 0;

    while (n !== 0) {
        ans++;
        n &= (n - 1);
    }

    return ans;
};

Detailed Explanation

Understanding the Problem

The objective of this problem is to calculate the Hamming weight of an unsigned integer n, which is the number of 1s present in its binary representation. This type of bit manipulation problem is common in systems programming, embedded development, and low-level algorithm design.

Bitwise Counting Strategy

To count the number of 1 bits efficiently, we use a bit manipulation trick involving the operation n & (n - 1). This operation removes the rightmost 1 bit from n. Each time this operation is performed, it reduces the number of 1s by one.

We initialize a counter ans to 0. Then, in a loop that continues while n ≠ 0:

We increment ans by 1 to count the current 1 bit.
We update n to n & (n - 1), which clears the rightmost 1 bit.

This loop continues exactly as many times as there are 1 bits in the number, making it highly efficient.

Example Walkthrough

Suppose n = 11, which is 1011 in binary:

First iteration: n = 1011, increment ans = 1, then n = 1010
Second iteration: n = 1010, increment ans = 2, then n = 1000
Third iteration: n = 1000, increment ans = 3, then n = 0000

The loop ends when n becomes 0, and we return ans = 3, which correctly reflects the number of 1 bits in 11.

Why This Works

The expression n & (n - 1) works by flipping the rightmost 1 bit in n to 0. For example, subtracting 1 from a binary number inverts the rightmost 1 bit and turns all less significant bits into 1s. When this result is ANDed with the original number, the rightmost 1 is cleared. This process happens exactly k times, where k is the number of 1 bits in n, making the method both intuitive and performant.

Time and Space Complexity

Time Complexity: O(k), where k is the number of 1 bits in n. For a 32-bit integer, this means a maximum of 32 iterations, making it effectively O(1) in practice.

Space Complexity: O(1), since the algorithm uses only a single integer counter and modifies the input n without requiring additional data structures.

Conclusion

The number of 1 bits in a binary number can be counted efficiently using bit manipulation. By using the n & (n - 1) trick, we reduce the problem to a simple loop that directly corresponds to the number of set bits. This makes the approach both elegant and optimal for fixed-width integers, and it’s a great example of why understanding bitwise operations can lead to cleaner and faster solutions.

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