Conclusion:
The answer is use std::trunc(f) == f the time difference is insignificant when comparing all these methods. Even if the specific IEEE unwinding code we write in the example below is technically twice is fast we are only talking about 1 nano second faster.
The maintenance costs in the long run though would be significantly higher. So use a solution that is easier to read and understand by the maintainer is better.
Time in microseconds to complete 12,000,000 operations on a random set of numbers:
- IEEE breakdown: 18
std::trunc(f) == f 32std::floor(val) - val == 0 35((uint64_t)f) - f) == 0.0 38std::fmod(val, 1.0) == 0 87
The Working out of the conclusion.
A floating point number is two parts:
mantissa: The data part of the value.
exponent: a power to multiply it by.
such that:
value = mantissa * (2^exponent)
So the exponent is basically how many binary digits we are going to shift the "binary point" down the mantissa. A positive value shifts it right a negative value shifts it left. If all the digits to the right of the binary point are zero then we have an integer.
If we assume IEEE 754
We should note that this representation the value is normalized so that the most significant bit in the mantissa is shifted to be 1. Since this bit is always set it is not actually stored (the processor knows its there and compensates accordingly).
So:
If the exponent < 0 then you definitely do not have an integer as it can only be representing a fractional value. If the exponent >= <Number of bits In Mantissa> then there is definately no fractual part and it is an integer (though you may not be able to hold it in an int).
Otherwise we have to do some work. if the exponent >= 0 && exponent < <Number of bits In Mantissa> then you may be representing an integer if the mantissa is all zero in the bottom half (defined below).
Additional as part of the normalization 127 is added to the exponent (so that there are no negative values stored in the 8 bit exponent field).
#include <limits>
#include <iostream>
#include <cmath>
/*
* Bit 31 Sign
* Bits 30-23 Exponent
* Bits 22-00 Mantissa
*/
bool is_IEEE754_32BitFloat_AnInt(float val)
{
// Put the value in an int so we can do bitwise operations.
int valAsInt = *reinterpret_cast<int*>(&val);
// Remember to subtract 127 from the exponent (to get real value)
int exponent = ((valAsInt >> 23) & 0xFF) - 127;
int bitsInFraction = 23 - exponent;
int mask = exponent < 0
? 0x7FFFFFFF
: exponent > 23
? 0x00
: (1 << bitsInFraction) - 1;
return !(valAsInt & mask);
}
/*
* Bit 63 Sign
* Bits 62-52 Exponent
* Bits 51-00 Mantissa
*/
bool is_IEEE754_64BitFloat_AnInt(double val)
{
// Put the value in an long long so we can do bitwise operations.
uint64_t valAsInt = *reinterpret_cast<uint64_t*>(&val);
// Remember to subtract 1023 from the exponent (to get real value)
int exponent = ((valAsInt >> 52) & 0x7FF) - 1023;
int bitsInFraction = 52 - exponent;
uint64_t mask = exponent < 0
? 0x7FFFFFFFFFFFFFFFLL
: exponent > 52
? 0x00
: (1LL << bitsInFraction) - 1;
return !(valAsInt & mask);
}
bool is_Trunc_32BitFloat_AnInt(float val)
{
return (std::trunc(val) - val == 0.0F);
}
bool is_Trunc_64BitFloat_AnInt(double val)
{
return (std::trunc(val) - val == 0.0);
}
bool is_IntCast_64BitFloat_AnInt(double val)
{
return (uint64_t(val) - val == 0.0);
}
template<typename T, bool isIEEE = std::numeric_limits<T>::is_iec559>
bool isInt(T f);
template<>
bool isInt<float, true>(float f) {return is_IEEE754_32BitFloat_AnInt(f);}
template<>
bool isInt<double, true>(double f) {return is_IEEE754_64BitFloat_AnInt(f);}
template<>
bool isInt<float, false>(float f) {return is_Trunc_64BitFloat_AnInt(f);}
template<>
bool isInt<double, false>(double f) {return is_Trunc_64BitFloat_AnInt(f);}
int main()
{
double x = 16;
std::cout << x << "=> " << isInt(x) << "\n";
x = 16.4;
std::cout << x << "=> " << isInt(x) << "\n";
x = 123.0;
std::cout << x << "=> " << isInt(x) << "\n";
x = 0.0;
std::cout << x << "=> " << isInt(x) << "\n";
x = 2.0;
std::cout << x << "=> " << isInt(x) << "\n";
x = 4.0;
std::cout << x << "=> " << isInt(x) << "\n";
x = 5.0;
std::cout << x << "=> " << isInt(x) << "\n";
x = 1.0;
std::cout << x << "=> " << isInt(x) << "\n";
}
Results:
> ./a.out
16=> 1
16.4=> 0
123=> 1
0=> 1
2=> 1
4=> 1
5=> 1
1=> 1
Running Some Timing tests.
Test data was generated like this:
(for a in {1..3000000};do echo $RANDOM.$RANDOM;done ) > test.data
(for a in {1..3000000};do echo $RANDOM;done ) >> test.data
(for a in {1..3000000};do echo $RANDOM$RANDOM0000;done ) >> test.data
(for a in {1..3000000};do echo 0.$RANDOM;done ) >> test.data
Modified main() to run tests:
int main()
{
// ORIGINAL CODE still here.
// Added this trivial speed test.
std::ifstream testData("test.data"); // Generated a million random numbers
std::vector<double> test{std::istream_iterator<double>(testData), std::istream_iterator<double>()};
std::cout << "Data Size: " << test.size() << "\n";
int count1 = 0;
int count2 = 0;
int count3 = 0;
auto start = std::chrono::system_clock::now();
for(auto const& v: test)
{ count1 += is_IEEE754_64BitFloat_AnInt(v);
}
auto p1 = std::chrono::system_clock::now();
for(auto const& v: test)
{ count2 += is_Trunc_64BitFloat_AnInt(v);
}
auto p2 = std::chrono::system_clock::now();
for(auto const& v: test)
{ count3 += is_IntCast_64BitFloat_AnInt(v);
}
auto end = std::chrono::system_clock::now();
std::cout << "IEEE " << count1 << " Time: " << std::chrono::duration_cast<std::chrono::milliseconds>(p1 - start).count() << "\n";
std::cout << "Trunc " << count2 << " Time: " << std::chrono::duration_cast<std::chrono::milliseconds>(p2 - p1).count() << "\n";
std::cout << "Int Cast " << count3 << " Time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - p2).count() << "\n"; }
The tests show:
> ./a.out
16=> 1
16.4=> 0
123=> 1
0=> 1
2=> 1
4=> 1
5=> 1
1=> 1
Data Size: 12000000
IEEE 6000199 Time: 18
Trunc 6000199 Time: 32
Int Cast 6000199 Time: 38
The IEEE code (in this simple test) seem to beat the truncate method and generate the same result. BUT the amount of time is insignificant. Over 12 million calls we saw a difference in 14 milliseconds.
