Fpclassify

Defined in header <cmath>.

Description

Categorizes floating point value num into the following categories:

• zero,
• subnormal,
• normal,
• infinite,
• NAN,
• implementation-defined category

The library provides overloads of std::fpclassify for all cv-unqualified floating-point types as the type of the parameter num  (od C++23).

​Additional Overloads are provided for all integer types, which are treated as double.

Declarations

C++23

// 1)
constexpr int fpclassify( /* floating-point-type */ num );

Additional Overloads

// 2)
template< class Integer >
constexpr int fpclassify( Integer num );

C++11

// 1)
int fpclassify( float num );
// 2)
int fpclassify( double num );
// 3)
int fpclassify( long double num );

Additional Overloads

// 4)
template< class Integer >
int fpclassify( Integer num );

Parameters

​num - floating-point or integer value

Return value

One of FP_INFINITE, FP_NAN, FP_NORMAL, FP_SUBNORMAL, FP_ZERO or implementation-defined type, specifying the category of num.

Notes

The additional overloads are not required to be provided exactly as Additional Overloads.
They only need to be sufficient to ensure that for their argument num of integer type,
std::fpclassify(num) has the same effect as std::fpclassify(static_cast<double>(num)).

Examples

#include <cfloat>
#include <cmath>
#include <iostream>
 
auto show_classification(double x)
{
    switch (std::fpclassify(x))
    {
        case FP_INFINITE:
            return "Inf";
        case FP_NAN:
            return "NaN";
        case FP_NORMAL:
            return "normal";
        case FP_SUBNORMAL:
            return "subnormal";
        case FP_ZERO:
            return "zero";
        default:
            return "unknown";
    }
}
 
int main()
{
    std::cout 
        << "1.0/0.0 is " 
        << show_classification(1 / 0.0) << '\n'
        << "0.0/0.0 is " 
        << show_classification(0.0 / 0.0) << '\n'
        << "DBL_MIN/2 is " 
        << show_classification(DBL_MIN / 2) << '\n'
        << "-0.0 is " 
        << show_classification(-0.0) << '\n'
        << "1.0 is " 
        << show_classification(1.0) << '\n';
}

Result

1.0/0.0 is Inf
0.0/0.0 is NaN
DBL_MIN/2 is subnormal
-0.0 is zero
1.0 is normal