Hamming weight
From Wikipedia, the free encyclopedia
The Hamming weight of a string is the number of symbols that are different from the zero-symbol of the alphabet used.
It is thus equivalent to the Hamming distance from the all-zero string of the same length. For the most typical case, a string of bits, this is the number of 1's in the string. In this binary case, it is also called the population count, or popcount.
Contents |
[edit] Examples
| alphabet | string | hamming weight |
| 0,1 | 11101 | 4 |
| 0,1 | 11101000 | 4 |
| 0,1 | 00000000 | 0 |
| ' ',a-z | hello world | 10 |
[edit] History and usage
The Hamming weight is named after Richard Hamming. It is used in several disciplines including information theory, coding theory, and cryptography.
[edit] Efficient implementation
The population count of a bitstring is often needed in cryptography and other applications. The problem of how to implement it efficiently has been widely studied. Some processors have a single command to calculate it, and some have parallel operations on bit vectors. For processors lacking those features, the best solutions known are based on adding counts in a tree pattern. For example, to count the number of 1 bits in the 16-bit binary number A=0110110010111010, these operations can be done:
| Expression | Binary | Decimal | Comment |
| A | 0110110010111010 | The original number | |
| B = A & 01 01 01 01 01 01 01 01 | 01 00 01 00 00 01 00 00 | 1,0,1,0,0,1,0,0 | every other bit from A |
| C = (A >> 1) & 01 01 01 01 01 01 01 01 | 00 01 01 00 01 01 01 01 | 0,1,1,0,1,1,1,1 | the remaining bits from A |
| D = B + C | 01 01 10 00 01 10 01 01 | 1,1,2,0,1,2,1,1 | list giving # of 1s in each 2-bit piece of A |
| E = D & 0011 0011 0011 0011 | 0001 0000 0010 0001 | 1,0,2,1 | every other count from D |
| F = (D >> 2) & 0011 0011 0011 0011 | 0001 0010 0001 0001 | 1,2,1,1 | the remaining counts from D |
| G = E + F | 0010 0010 0011 0010 | 2,2,3,2 | list giving # of 1s in each 4-bit piece of A |
| H = G & 00001111 00001111 | 00000010 00000010 | 2,2 | every other count from G |
| I = (G >> 4) & 00001111 00001111 | 00000010 00000011 | 2,3 | the remaining counts from G |
| J = H + I | 00000100 00000101 | 4,5 | list giving # of 1s in each 8-bit piece of A |
| K = J & 0000000011111111 | 0000000000000101 | 5 | every other count from J |
| L = (J >> 8) & 0000000011111111 | 0000000000000100 | 4 | the remaining counts from J |
| M = K + L | 0000000000001001 | 9 | the final answer |
Here, the operations are as in C, so X >> Y means to shift X right by Y bits, X & Y means the bitwise AND of X and Y, and + is ordinary addition. The best algorithms known for this problem are based on the concept illustrated above and are given here:
//types and constants used in the functions below
typedef unsigned __int64 uint64; //assume this gives 64-bits
const uint64 m1 = 0x5555555555555555; //binary: 0101...
const uint64 m2 = 0x3333333333333333; //binary: 00110011..
const uint64 m4 = 0x0f0f0f0f0f0f0f0f; //binary: 4 zeros, 4 ones ...
const uint64 m8 = 0x00ff00ff00ff00ff; //binary: 8 zeros, 8 ones ...
const uint64 m16 = 0x0000ffff0000ffff; //binary: 16 zeros, 16 ones ...
const uint64 m32 = 0x00000000ffffffff; //binary: 32 zeros, 32 ones
const uint64 hff = 0xffffffffffffffff; //binary: all ones
const uint64 h01 = 0x0101010101010101; //the sum of 256 to the power of 0,1,2,3...
//This is a naive implementation, shown for comparison,
//and to help in understanding the better functions.
//It uses 24 arithmetic operations (shift, add, and).
int popcount_1(uint64 x) {
x = (x & m1 ) + ((x >> 1) & m1 ); //put count of each 2 bits into those 2 bits
x = (x & m2 ) + ((x >> 2) & m2 ); //put count of each 4 bits into those 4 bits
x = (x & m4 ) + ((x >> 4) & m4 ); //put count of each 8 bits into those 8 bits
x = (x & m8 ) + ((x >> 8) & m8 ); //put count of each 16 bits into those 16 bits
x = (x & m16) + ((x >> 16) & m16); //put count of each 32 bits into those 32 bits
x = (x & m32) + ((x >> 32) & m32); //put count of each 64 bits into those 64 bits
return x;
}
//This uses fewer arithmetic operations than any other known
//implementation on machines with slow multiplication.
//It uses 17 arithmetic operations.
int popcount_2(uint64 x) {
x -= (x >> 1) & m1; //put count of each 2 bits into those 2 bits
x = (x & m2) + ((x >> 2) & m2); //put count of each 4 bits into those 4 bits
x = (x + (x >> 4)) & m4; //put count of each 8 bits into those 8 bits
x += x >> 8; //put count of each 16 bits into their lowest 8 bits
x += x >> 16; //put count of each 32 bits into their lowest 8 bits
x += x >> 32; //put count of each 64 bits into their lowest 8 bits
return x & 0x3f;
}
//This uses fewer arithmetic operations than any other known
//implementation on machines with fast multiplication.
//It uses 12 arithmetic operations, one of which is a multiply.
int popcount_3(uint64 x) {
x -= (x >> 1) & m1; //put count of each 2 bits into those 2 bits
x = (x & m2) + ((x >> 2) & m2); //put count of each 4 bits into those 4 bits
x = (x + (x >> 4)) & m4; //put count of each 8 bits into those 8 bits
return (x * h01)>>56; //returns left 8 bits of x + (x<<8) + (x<<16) + (x<<24) + ...
}
The above implementations have the best worst-case behavior of any known algorithm. However, if a number is known to have most of its bits 0 (or most of its bits 1), then there are faster algorithms. They are based on the fact that the bitwise AND of a number X and the number X-1 will be the same as X with the rightmost 1 bit set to 0. For example:
| Expression | Value |
| X | 0 1 0 0 0 1 0 0 0 1 0 0 0 0 |
| X - 1 | 0 1 0 0 0 1 0 0 0 0 1 1 1 1 |
| X & (X - 1) | 0 1 0 0 0 1 0 0 0 0 0 0 0 0 |
Subtracting 1 changes the rightmost string of 0s to 1s, and changes the rightmost 1 to a 0. The AND then removes that rightmost 1. If X originally had N bits that were 1, then after only N iterations of this operation, X will be reduced to zero. The following are based on this principle.
//This is better when most bits in x are 0
//It uses 3 arithmetic operations and one comparison/branch per "1" bit in x.
int popcount_4(uint64 x) {
uint64 count;
for (count=0; x; count++)
x &= x-1;
return count;
}
//This is better if most bits in x are 0.
//It uses 2 arithmetic operations and one comparison/branch per "1" bit in x.
//It is the same as the previous function, but with the loop unrolled.
#define f(y) if ((x &= x-1) == 0) return y;
int popcount_5(uint64 x) {
if (x == 0) return 0;
f( 1) f( 2) f( 3) f( 4) f( 5) f( 6) f( 7) f( 8)
f( 9) f(10) f(11) f(12) f(13) f(14) f(15) f(16)
f(17) f(18) f(19) f(20) f(21) f(22) f(23) f(24)
f(25) f(26) f(27) f(28) f(29) f(30) f(31) f(32)
f(33) f(34) f(35) f(36) f(37) f(38) f(39) f(40)
f(41) f(42) f(43) f(44) f(45) f(46) f(47) f(48)
f(49) f(50) f(51) f(52) f(53) f(54) f(55) f(56)
f(57) f(58) f(59) f(60) f(61) f(62) f(63)
return 64;
}
//Use this instead if most bits in x are 1 instead of 0
#define f(y) if ((x |= x+1) == hff) return 64-y;
[edit] Language Support
In C++ STL, the bit-array data structure bitset has a count() method that counts the number of bits that are set.
In Java, the growable bit-array data structure BitSet has a BitSet.cardinality() method that counts the number of bits that are set. In addition, there are Integer.bitCount(int) and Long.bitCount(long) functions to count bits in primitive 32-bit and 64-bit integers, respectively. Also, the BigInteger arbitrary-precision integer class also has a BigInteger.bitCount() method that counts bits.
[edit] See also
[edit] External links
- Aggregate Magic Algorithms. Optimized population count and other algorithms explained with sample code.
- HACKMEM item 169. Population count assembly code for the PDP/6-10.
- Bit Twiddling Hacks Several algorithms with code for counting bits set.
