比较两个双精度浮点数或两个浮点数最有效的方法是什么?
简单地这样做是不正确的:
bool CompareDoubles1 (double A, double B)
{
return A == B;
}
比如:
bool CompareDoubles2 (double A, double B)
{
diff = A - B;
return (diff < EPSILON) && (-diff < EPSILON);
}
似乎是浪费加工。
有人知道更聪明的浮点比较器吗?
我使用这个代码。不像上面的答案,这允许一个人
给出一个在代码注释中解释的abs_relative_error。
第一个版本比较复数,使错误
可以用两个矢量之间的夹角来解释
在复平面上具有相同的长度(这给出了一点
洞察力)。然后是2实数的正确公式
数字。
https://github.com/CarloWood/ai-utils/blob/master/almost_equal.h
后者是
template<class T>
typename std::enable_if<std::is_floating_point<T>::value, bool>::type
almost_equal(T x, T y, T const abs_relative_error)
{
return 2 * std::abs(x - y) <= abs_relative_error * std::abs(x + y);
}
其中abs_relative_error基本上(两倍)是文献中最接近定义的绝对值:相对错误。但这只是名字的选择。
我认为在复平面中最明显的是。如果|x| = 1, y在x周围形成一个直径为abs_relative_error的圆,则认为两者相等。
在这个版本中,你可以检查,这些数字之间的差异并不比某些分数(比如,0.0001%)更大:
bool floatApproximatelyEquals(const float a, const float b) {
if (b == 0.) return a == 0.; // preventing division by zero
return abs(1. - a / b) < 1e-6;
}
请注意Sneftel关于浮动可能的分数限制的评论。
还要注意的是,它不同于使用绝对的epsilon的方法——这里你不需要担心“数量级”——数字可能是,比如说1e100,或者1e-100,它们总是会被一致地比较,而且你不必为每一种情况更新epsilon。
在https://en.cppreference.com/w/cpp/types/numeric_limits/epsilon上找到了另一个有趣的实现
#include <cmath>
#include <limits>
#include <iomanip>
#include <iostream>
#include <type_traits>
#include <algorithm>
template<class T>
typename std::enable_if<!std::numeric_limits<T>::is_integer, bool>::type
almost_equal(T x, T y, int ulp)
{
// the machine epsilon has to be scaled to the magnitude of the values used
// and multiplied by the desired precision in ULPs (units in the last place)
return std::fabs(x-y) <= std::numeric_limits<T>::epsilon() * std::fabs(x+y) * ulp
// unless the result is subnormal
|| std::fabs(x-y) < std::numeric_limits<T>::min();
}
int main()
{
double d1 = 0.2;
double d2 = 1 / std::sqrt(5) / std::sqrt(5);
std::cout << std::fixed << std::setprecision(20)
<< "d1=" << d1 << "\nd2=" << d2 << '\n';
if(d1 == d2)
std::cout << "d1 == d2\n";
else
std::cout << "d1 != d2\n";
if(almost_equal(d1, d2, 2))
std::cout << "d1 almost equals d2\n";
else
std::cout << "d1 does not almost equal d2\n";
}
我最终花了相当多的时间在这个伟大的线程通过材料。我怀疑每个人都想花这么多时间,所以我将强调我所学到的总结和我实施的解决方案。
快速的总结
Is 1e-8 approximately same as 1e-16? If you are looking at noisy sensor data then probably yes but if you are doing molecular simulation then may be not! Bottom line: You always need to think of tolerance value in context of specific function call and not just make it generic app-wide hard-coded constant.
For general library functions, it's still nice to have parameter with default tolerance. A typical choice is numeric_limits::epsilon() which is same as FLT_EPSILON in float.h. This is however problematic because epsilon for comparing values like 1.0 is not same as epsilon for values like 1E9. The FLT_EPSILON is defined for 1.0.
The obvious implementation to check if number is within tolerance is fabs(a-b) <= epsilon however this doesn't work because default epsilon is defined for 1.0. We need to scale epsilon up or down in terms of a and b.
There are two solution to this problem: either you set epsilon proportional to max(a,b) or you can get next representable numbers around a and then see if b falls into that range. The former is called "relative" method and later is called ULP method.
Both methods actually fails anyway when comparing with 0. In this case, application must supply correct tolerance.
实用函数实现(c++ 11)
//implements relative method - do not use for comparing with zero
//use this most of the time, tolerance needs to be meaningful in your context
template<typename TReal>
static bool isApproximatelyEqual(TReal a, TReal b, TReal tolerance = std::numeric_limits<TReal>::epsilon())
{
TReal diff = std::fabs(a - b);
if (diff <= tolerance)
return true;
if (diff < std::fmax(std::fabs(a), std::fabs(b)) * tolerance)
return true;
return false;
}
//supply tolerance that is meaningful in your context
//for example, default tolerance may not work if you are comparing double with float
template<typename TReal>
static bool isApproximatelyZero(TReal a, TReal tolerance = std::numeric_limits<TReal>::epsilon())
{
if (std::fabs(a) <= tolerance)
return true;
return false;
}
//use this when you want to be on safe side
//for example, don't start rover unless signal is above 1
template<typename TReal>
static bool isDefinitelyLessThan(TReal a, TReal b, TReal tolerance = std::numeric_limits<TReal>::epsilon())
{
TReal diff = a - b;
if (diff < tolerance)
return true;
if (diff < std::fmax(std::fabs(a), std::fabs(b)) * tolerance)
return true;
return false;
}
template<typename TReal>
static bool isDefinitelyGreaterThan(TReal a, TReal b, TReal tolerance = std::numeric_limits<TReal>::epsilon())
{
TReal diff = a - b;
if (diff > tolerance)
return true;
if (diff > std::fmax(std::fabs(a), std::fabs(b)) * tolerance)
return true;
return false;
}
//implements ULP method
//use this when you are only concerned about floating point precision issue
//for example, if you want to see if a is 1.0 by checking if its within
//10 closest representable floating point numbers around 1.0.
template<typename TReal>
static bool isWithinPrecisionInterval(TReal a, TReal b, unsigned int interval_size = 1)
{
TReal min_a = a - (a - std::nextafter(a, std::numeric_limits<TReal>::lowest())) * interval_size;
TReal max_a = a + (std::nextafter(a, std::numeric_limits<TReal>::max()) - a) * interval_size;
return min_a <= b && max_a >= b;
}