C++11标准模板（STL）- 常用数学函数 - 分类及比较 - 对给定的浮点值分类（std::fpclassify）
常用数学函数
对给定的浮点值分类
std::fpclassify| 定义于头文件 | ||
| #define fpclassify(arg) /* implementation defined */ | (C99 起) |
归类浮点值 arg 到下列类别中：零、非正规、正规、无穷大、 NaN 或实现定义类别。该宏返回整数值。
忽略 FLT_EVAL_METHOD ：即使以多于参数类型的范围和精度对它求值，首先仍将它转换到其语义类型，然后分类基于该类型：正规的 long double 值可能在转换到 double 时变为非正规，而在转换到 float 时变为零。
参数
| arg | - | 浮点值 |
返回值
指明 arg 类别的 FP_INFINITE 、 FP_NAN 、 FP_NORMAL 、 FP_SUBNORMAL 、 FP_ZERO 或实现定义类型之一。
#define FP_NAN 0x0100
#define FP_NORMAL 0x0400
#define FP_INFINITE (FP_NAN | FP_NORMAL)
#define FP_ZERO 0x4000
#define FP_SUBNORMAL (FP_NORMAL | FP_ZERO)可能实现
namespace GGX
{
using namespace std;
constexpr int
fpclassify(float __x)
{return __builtin_fpclassify(FP_NAN, FP_INFINITE, FP_NORMAL,FP_SUBNORMAL, FP_ZERO, __x);
}constexpr int
fpclassify(double __x)
{return __builtin_fpclassify(FP_NAN, FP_INFINITE, FP_NORMAL,FP_SUBNORMAL, FP_ZERO, __x);
}constexpr int
fpclassify(long double __x)
{return __builtin_fpclassify(FP_NAN, FP_INFINITE, FP_NORMAL,FP_SUBNORMAL, FP_ZERO, __x);
}template<typename _Tp>
constexpr typename __gnu_cxx::__enable_if<__is_integer<_Tp>::__value,int>::__typefpclassify(_Tp __x)
{return __x != 0 ? FP_NORMAL : FP_ZERO;
}
}调用示例
#include <iostream>
#include <cstdlib>
#include <typeinfo>
#include <cinttypes>
#include <cmath>
#include <math.h>
#include <tgmath.h>
#include <stdio.h>
#include <math.h>
#include <float.h>const char *show_classification(double x)
{//归类浮点值 arg 到下列类别中：零、非正规、正规、无穷大、 NaN 或实现定义类别。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(void)
{std::cout << "show_classification(1.0/0.0) is " << show_classification(1 / 0.0) << std::endl;std::cout << "show_classification(0.0/0.0) is is " << show_classification(0.0 / 0.0) << std::endl;std::cout << "show_classification(DBL_MIN/2) is is " << show_classification(DBL_MIN / 2) << std::endl;std::cout << "show_classification(-0.0 is is) " << show_classification(-0.0) << std::endl;std::cout << "show_classification(1.0 is is) " << show_classification(1.0) << std::endl;std::cout << std::endl;std::cout << "std::fpclassify(1.0/0.0) is " << std::fpclassify(1 / 0.0) << std::endl;std::cout << "std::fpclassify(0.0/0.0) is is " << std::fpclassify(0.0 / 0.0) << std::endl;std::cout << "std::fpclassify(DBL_MIN/2) is is " << std::fpclassify(DBL_MIN / 2) << std::endl;std::cout << "std::fpclassify(-0.0) is " << std::fpclassify(-0.0) << std::endl;std::cout << "std::fpclassify(1.0) is " << std::fpclassify(1.0) << std::endl;std::cout << std::endl;std::cout << std::hex;std::cout << "std::fpclassify(1.0/0.0) is " << std::fpclassify(1 / 0.0) << std::endl;std::cout << "std::fpclassify(0.0/0.0) is is " << std::fpclassify(0.0 / 0.0) << std::endl;std::cout << "std::fpclassify(DBL_MIN/2) is is " << std::fpclassify(DBL_MIN / 2) << std::endl;std::cout << "std::fpclassify(-0.0) is " << std::fpclassify(-0.0) << std::endl;std::cout << "std::fpclassify(1.0) is " << std::fpclassify(1.0) << std::endl;std::cout << std::endl;printf("show_classification(1.0/0.0) is %s\n", show_classification(1 / 0.0));printf("show_classification(0.0/0.0) is %s\n", show_classification(0.0 / 0.0));printf("show_classification(DBL_MIN/2) is %s\n", show_classification(DBL_MIN / 2));printf("show_classification(-0.0) is %s\n", show_classification(-0.0));printf("show_classification(1.0) is %s\n", show_classification(1.0));std::cout << std::endl;return 0;
}
输出
show_classification(1.0/0.0) is Inf
show_classification(0.0/0.0) is is NaN
show_classification(DBL_MIN/2) is is subnormal
show_classification(-0.0 is is) zero
show_classification(1.0 is is) normalstd::fpclassify(1.0/0.0) is 1280
std::fpclassify(0.0/0.0) is is 256
std::fpclassify(DBL_MIN/2) is is 17408
std::fpclassify(-0.0) is 16384
std::fpclassify(1.0) is 1024std::fpclassify(1.0/0.0) is 500
std::fpclassify(0.0/0.0) is is 100
std::fpclassify(DBL_MIN/2) is is 4400
std::fpclassify(-0.0) is 4000
std::fpclassify(1.0) is 400show_classification(1.0/0.0) is Inf
show_classification(0.0/0.0) is NaN
show_classification(DBL_MIN/2) is subnormal
show_classification(-0.0) is zero
show_classification(1.0) is normal