feat: add Count Set Bits algorithm (#7072)

* feat: add Count Set Bits algorithm (issue #6931)

* fix: correct CountSetBits algorithm logic

* style: apply clang-format to CountSetBits files

* fix: correct test expectations for CountSetBits

* fix: correct test expectations for CountSetBits
This commit is contained in:
GOPISETTI NAVADEEP
2025-11-16 17:24:43 +05:30
committed by GitHub
parent 3979e824b7
commit 93811614b8
2 changed files with 108 additions and 78 deletions

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@@ -1,79 +1,79 @@
package com.thealgorithms.bitmanipulation;
public class CountSetBits {
/**
* Utility class to count total set bits from 1 to N
* A set bit is a bit in binary representation that is 1
*
* @author navadeep
*/
public final class CountSetBits {
/**
* The below algorithm is called as Brian Kernighan's algorithm
* We can use Brian Kernighans algorithm to improve the above naive algorithms performance.
The idea is to only consider the set bits of an integer by turning off its rightmost set bit
(after counting it), so the next iteration of the loop considers the next rightmost bit.
The expression n & (n-1) can be used to turn off the rightmost set bit of a number n. This
works as the expression n-1 flips all the bits after the rightmost set bit of n, including the
rightmost set bit itself. Therefore, n & (n-1) results in the last bit flipped of n.
For example, consider number 52, which is 00110100 in binary, and has a total 3 bits set.
1st iteration of the loop: n = 52
00110100 & (n)
00110011 (n-1)
~~~~~~~~
00110000
2nd iteration of the loop: n = 48
00110000 & (n)
00101111 (n-1)
~~~~~~~~
00100000
3rd iteration of the loop: n = 32
00100000 & (n)
00011111 (n-1)
~~~~~~~~
00000000 (n = 0)
* @param num takes Long number whose number of set bit is to be found
* @return the count of set bits in the binary equivalent
*/
public long countSetBits(long num) {
long cnt = 0;
while (num > 0) {
cnt++;
num &= (num - 1);
}
return cnt;
private CountSetBits() {
// Utility class, prevent instantiation
}
/**
* This approach takes O(1) running time to count the set bits, but requires a pre-processing.
* Counts total number of set bits in all numbers from 1 to n
* Time Complexity: O(log n)
*
* So, we divide our 32-bit input into 8-bit chunks, with four chunks. We have 8 bits in each chunk.
*
* Then the range is from 0-255 (0 to 2^7).
* So, we may need to count set bits from 0 to 255 in individual chunks.
*
* @param num takes a long number
* @return the count of set bits in the binary equivalent
* @param n the upper limit (inclusive)
* @return total count of set bits from 1 to n
* @throws IllegalArgumentException if n is negative
*/
public int lookupApproach(int num) {
int[] table = new int[256];
table[0] = 0;
for (int i = 1; i < 256; i++) {
table[i] = (i & 1) + table[i >> 1]; // i >> 1 equals to i/2
public static int countSetBits(int n) {
if (n < 0) {
throw new IllegalArgumentException("Input must be non-negative");
}
int res = 0;
for (int i = 0; i < 4; i++) {
res += table[num & 0xff];
num >>= 8;
if (n == 0) {
return 0;
}
return res;
// Find the largest power of 2 <= n
int x = largestPowerOf2InNumber(n);
// Total bits at position x: x * 2^(x-1)
int bitsAtPositionX = x * (1 << (x - 1));
// Remaining numbers after 2^x
int remainingNumbers = n - (1 << x) + 1;
// Recursively count for the rest
int rest = countSetBits(n - (1 << x));
return bitsAtPositionX + remainingNumbers + rest;
}
/**
* Finds the position of the most significant bit in n
*
* @param n the number
* @return position of MSB (0-indexed from right)
*/
private static int largestPowerOf2InNumber(int n) {
int position = 0;
while ((1 << position) <= n) {
position++;
}
return position - 1;
}
/**
* Alternative naive approach - counts set bits by iterating through all numbers
* Time Complexity: O(n log n)
*
* @param n the upper limit (inclusive)
* @return total count of set bits from 1 to n
*/
public static int countSetBitsNaive(int n) {
if (n < 0) {
throw new IllegalArgumentException("Input must be non-negative");
}
int count = 0;
for (int i = 1; i <= n; i++) {
count += Integer.bitCount(i);
}
return count;
}
}

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@@ -1,26 +1,56 @@
package com.thealgorithms.bitmanipulation;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertThrows;
import org.junit.jupiter.api.Test;
public class CountSetBitsTest {
class CountSetBitsTest {
@Test
void testSetBits() {
CountSetBits csb = new CountSetBits();
assertEquals(1L, csb.countSetBits(16));
assertEquals(4, csb.countSetBits(15));
assertEquals(5, csb.countSetBits(10000));
assertEquals(5, csb.countSetBits(31));
void testCountSetBitsZero() {
assertEquals(0, CountSetBits.countSetBits(0));
}
@Test
void testSetBitsLookupApproach() {
CountSetBits csb = new CountSetBits();
assertEquals(1L, csb.lookupApproach(16));
assertEquals(4, csb.lookupApproach(15));
assertEquals(5, csb.lookupApproach(10000));
assertEquals(5, csb.lookupApproach(31));
void testCountSetBitsOne() {
assertEquals(1, CountSetBits.countSetBits(1));
}
@Test
void testCountSetBitsSmallNumber() {
assertEquals(4, CountSetBits.countSetBits(3)); // 1(1) + 10(1) + 11(2) = 4
}
@Test
void testCountSetBitsFive() {
assertEquals(7, CountSetBits.countSetBits(5)); // 1 + 1 + 2 + 1 + 2 = 7
}
@Test
void testCountSetBitsTen() {
assertEquals(17, CountSetBits.countSetBits(10));
}
@Test
void testCountSetBitsLargeNumber() {
assertEquals(42, CountSetBits.countSetBits(20)); // Changed from 93 to 42
}
@Test
void testCountSetBitsPowerOfTwo() {
assertEquals(13, CountSetBits.countSetBits(8)); // Changed from 9 to 13
}
@Test
void testCountSetBitsNegativeInput() {
assertThrows(IllegalArgumentException.class, () -> CountSetBits.countSetBits(-1));
}
@Test
void testNaiveApproachMatchesOptimized() {
for (int i = 0; i <= 100; i++) {
assertEquals(CountSetBits.countSetBitsNaive(i), CountSetBits.countSetBits(i), "Mismatch at n = " + i);
}
}
}