浮动和双重比较最有效的方法是什么？

与epsilon值的比较是大多数人所做的(即使在游戏编程中).

你应该稍微改变你的实现:

bool AreSame(double a, double b)
{
    return fabs(a - b) < EPSILON;
}

编辑:Christer在最近的博客文章中添加了一堆有关此主题的精彩信息.请享用.

我发现Google C++测试框架包含一个很好的跨平台基于模板的AlmostEqual2sComplement实现,它可以在双精度和浮点数上运行.鉴于它是根据BSD许可证发布的,只要您保留许可证,在您自己的代码中使用它应该没有问题.我从http://code.google.com/p/googletest/source/browse/trunk/include/gtest/internal/gtest-internal.h https://github.com/google/googletest/blob中提取了以下代码/master/googletest/include/gtest/internal/gtest-internal.h并在顶部添加了许可证.

一定要#define GTEST_OS_WINDOWS到某个值(或者将代码改为适合代码库的代码 - 毕竟它是BSD许可的).

用法示例:

double left  = // something
double right = // something
const FloatingPoint lhs(left), rhs(right);

if (lhs.AlmostEquals(rhs)) {
  //they're equal!
}

这是代码:

// Copyright 2005, Google Inc.
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
//     * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//     * Redistributions in binary form must reproduce the above
// copyright notice, this list of conditions and the following disclaimer
// in the documentation and/or other materials provided with the
// distribution.
//     * Neither the name of Google Inc. nor the names of its
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Authors: wan@google.com (Zhanyong Wan), eefacm@gmail.com (Sean Mcafee)
//
// The Google C++ Testing Framework (Google Test)


// This template class serves as a compile-time function from size to
// type.  It maps a size in bytes to a primitive type with that
// size. e.g.
//
//   TypeWithSize<4>::UInt
//
// is typedef-ed to be unsigned int (unsigned integer made up of 4
// bytes).
//
// Such functionality should belong to STL, but I cannot find it
// there.
//
// Google Test uses this class in the implementation of floating-point
// comparison.
//
// For now it only handles UInt (unsigned int) as that's all Google Test
// needs.  Other types can be easily added in the future if need
// arises.
template 
class TypeWithSize {
 public:
  // This prevents the user from using TypeWithSize with incorrect
  // values of N.
  typedef void UInt;
};

// The specialization for size 4.
template <>
class TypeWithSize<4> {
 public:
  // unsigned int has size 4 in both gcc and MSVC.
  //
  // As base/basictypes.h doesn't compile on Windows, we cannot use
  // uint32, uint64, and etc here.
  typedef int Int;
  typedef unsigned int UInt;
};

// The specialization for size 8.
template <>
class TypeWithSize<8> {
 public:
#if GTEST_OS_WINDOWS
  typedef __int64 Int;
  typedef unsigned __int64 UInt;
#else
  typedef long long Int;  // NOLINT
  typedef unsigned long long UInt;  // NOLINT
#endif  // GTEST_OS_WINDOWS
};


// This template class represents an IEEE floating-point number
// (either single-precision or double-precision, depending on the
// template parameters).
//
// The purpose of this class is to do more sophisticated number
// comparison.  (Due to round-off error, etc, it's very unlikely that
// two floating-points will be equal exactly.  Hence a naive
// comparison by the == operation often doesn't work.)
//
// Format of IEEE floating-point:
//
//   The most-significant bit being the leftmost, an IEEE
//   floating-point looks like
//
//     sign_bit exponent_bits fraction_bits
//
//   Here, sign_bit is a single bit that designates the sign of the
//   number.
//
//   For float, there are 8 exponent bits and 23 fraction bits.
//
//   For double, there are 11 exponent bits and 52 fraction bits.
//
//   More details can be found at
//   http://en.wikipedia.org/wiki/IEEE_floating-point_standard.
//
// Template parameter:
//
//   RawType: the raw floating-point type (either float or double)
template 
class FloatingPoint {
 public:
  // Defines the unsigned integer type that has the same size as the
  // floating point number.
  typedef typename TypeWithSize::UInt Bits;

  // Constants.

  // # of bits in a number.
  static const size_t kBitCount = 8*sizeof(RawType);

  // # of fraction bits in a number.
  static const size_t kFractionBitCount =
    std::numeric_limits::digits - 1;

  // # of exponent bits in a number.
  static const size_t kExponentBitCount = kBitCount - 1 - kFractionBitCount;

  // The mask for the sign bit.
  static const Bits kSignBitMask = static_cast(1) << (kBitCount - 1);

  // The mask for the fraction bits.
  static const Bits kFractionBitMask =
    ~static_cast(0) >> (kExponentBitCount + 1);

  // The mask for the exponent bits.
  static const Bits kExponentBitMask = ~(kSignBitMask | kFractionBitMask);

  // How many ULP's (Units in the Last Place) we want to tolerate when
  // comparing two numbers.  The larger the value, the more error we
  // allow.  A 0 value means that two numbers must be exactly the same
  // to be considered equal.
  //
  // The maximum error of a single floating-point operation is 0.5
  // units in the last place.  On Intel CPU's, all floating-point
  // calculations are done with 80-bit precision, while double has 64
  // bits.  Therefore, 4 should be enough for ordinary use.
  //
  // See the following article for more details on ULP:
  // http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm.
  static const size_t kMaxUlps = 4;

  // Constructs a FloatingPoint from a raw floating-point number.
  //
  // On an Intel CPU, passing a non-normalized NAN (Not a Number)
  // around may change its bits, although the new value is guaranteed
  // to be also a NAN.  Therefore, don't expect this constructor to
  // preserve the bits in x when x is a NAN.
  explicit FloatingPoint(const RawType& x) { u_.value_ = x; }

  // Static methods

  // Reinterprets a bit pattern as a floating-point number.
  //
  // This function is needed to test the AlmostEquals() method.
  static RawType ReinterpretBits(const Bits bits) {
    FloatingPoint fp(0);
    fp.u_.bits_ = bits;
    return fp.u_.value_;
  }

  // Returns the floating-point number that represent positive infinity.
  static RawType Infinity() {
    return ReinterpretBits(kExponentBitMask);
  }

  // Non-static methods

  // Returns the bits that represents this number.
  const Bits &bits() const { return u_.bits_; }

  // Returns the exponent bits of this number.
  Bits exponent_bits() const { return kExponentBitMask & u_.bits_; }

  // Returns the fraction bits of this number.
  Bits fraction_bits() const { return kFractionBitMask & u_.bits_; }

  // Returns the sign bit of this number.
  Bits sign_bit() const { return kSignBitMask & u_.bits_; }

  // Returns true iff this is NAN (not a number).
  bool is_nan() const {
    // It's a NAN if the exponent bits are all ones and the fraction
    // bits are not entirely zeros.
    return (exponent_bits() == kExponentBitMask) && (fraction_bits() != 0);
  }

  // Returns true iff this number is at most kMaxUlps ULP's away from
  // rhs.  In particular, this function:
  //
  //   - returns false if either number is (or both are) NAN.
  //   - treats really large numbers as almost equal to infinity.
  //   - thinks +0.0 and -0.0 are 0 DLP's apart.
  bool AlmostEquals(const FloatingPoint& rhs) const {
    // The IEEE standard says that any comparison operation involving
    // a NAN must return false.
    if (is_nan() || rhs.is_nan()) return false;

    return DistanceBetweenSignAndMagnitudeNumbers(u_.bits_, rhs.u_.bits_)
        <= kMaxUlps;
  }

 private:
  // The data type used to store the actual floating-point number.
  union FloatingPointUnion {
    RawType value_;  // The raw floating-point number.
    Bits bits_;      // The bits that represent the number.
  };

  // Converts an integer from the sign-and-magnitude representation to
  // the biased representation.  More precisely, let N be 2 to the
  // power of (kBitCount - 1), an integer x is represented by the
  // unsigned number x + N.
  //
  // For instance,
  //
  //   -N + 1 (the most negative number representable using
  //          sign-and-magnitude) is represented by 1;
  //   0      is represented by N; and
  //   N - 1  (the biggest number representable using
  //          sign-and-magnitude) is represented by 2N - 1.
  //
  // Read http://en.wikipedia.org/wiki/Signed_number_representations
  // for more details on signed number representations.
  static Bits SignAndMagnitudeToBiased(const Bits &sam) {
    if (kSignBitMask & sam) {
      // sam represents a negative number.
      return ~sam + 1;
    } else {
      // sam represents a positive number.
      return kSignBitMask | sam;
    }
  }

  // Given two numbers in the sign-and-magnitude representation,
  // returns the distance between them as an unsigned number.
  static Bits DistanceBetweenSignAndMagnitudeNumbers(const Bits &sam1,
                                                     const Bits &sam2) {
    const Bits biased1 = SignAndMagnitudeToBiased(sam1);
    const Bits biased2 = SignAndMagnitudeToBiased(sam2);
    return (biased1 >= biased2) ? (biased1 - biased2) : (biased2 - biased1);
  }

  FloatingPointUnion u_;
};

编辑:这篇文章是4岁.它可能仍然有效,代码很好,但有些人发现了改进.最好AlmostEquals从Google Test源代码中获取最新版本,而不是我在此处粘贴的版本.

比较浮点数取决于上下文.因为即使改变操作顺序也会产生不同的结果,重要的是要知道你想要数字的"相等"程度.

在查看浮点比较时,比较布鲁斯道森的浮点数是一个很好的起点.

以下定义来自Knuth的计算机编程艺术:

bool approximatelyEqual(float a, float b, float epsilon)
{
    return fabs(a - b) <= ( (fabs(a) < fabs(b) ? fabs(b) : fabs(a)) * epsilon);
}

bool essentiallyEqual(float a, float b, float epsilon)
{
    return fabs(a - b) <= ( (fabs(a) > fabs(b) ? fabs(b) : fabs(a)) * epsilon);
}

bool definitelyGreaterThan(float a, float b, float epsilon)
{
    return (a - b) > ( (fabs(a) < fabs(b) ? fabs(b) : fabs(a)) * epsilon);
}

bool definitelyLessThan(float a, float b, float epsilon)
{
    return (b - a) > ( (fabs(a) < fabs(b) ? fabs(b) : fabs(a)) * epsilon);
}

当然,选择epsilon取决于上下文,并确定您希望数字的相等程度.

比较浮点数的另一种方法是查看数字的ULP(最后位置的单位).虽然没有专门处理比较,但是每个计算机科学家应该知道的浮点数是一个很好的资源,可以用来理解浮点的工作原理和陷阱是什么,包括ULP是什么.

有关更深入的方法,请参阅比较浮点数.以下是该链接的代码段:

// Usable AlmostEqual function    
bool AlmostEqual2sComplement(float A, float B, int maxUlps)    
{    
    // Make sure maxUlps is non-negative and small enough that the    
    // default NAN won't compare as equal to anything.    
    assert(maxUlps > 0 && maxUlps < 4 * 1024 * 1024);    
    int aInt = *(int*)&A;    
    // Make aInt lexicographically ordered as a twos-complement int    
    if (aInt < 0)    
        aInt = 0x80000000 - aInt;    
    // Make bInt lexicographically ordered as a twos-complement int    
    int bInt = *(int*)&B;    
    if (bInt < 0)    
        bInt = 0x80000000 - bInt;    
    int intDiff = abs(aInt - bInt);    
    if (intDiff <= maxUlps)    
        return true;    
    return false;    
}

在C++中获取epsilon的便携方式是

#include 
std::numeric_limits::epsilon()

然后比较功能变为

#include 
#include 

bool AreSame(double a, double b) {
    return std::fabs(a - b) < std::numeric_limits::epsilon();
}

认识到这是一个旧线程,但本文是我在比较浮点数时发现的最直接的文章之一,如果你想探索更多,它还有更详细的参考资料,主要网站涵盖了一系列问题处理浮点数浮点指南:比较.

我们可以在浮点公差中找到一篇更实用的文章,并注意到有绝对容差测试,可以归结为C++:

bool absoluteToleranceCompare(double x, double y)
{
    return std::fabs(x - y) <= std::numeric_limits::epsilon() ;
}

和相对耐受性测试:

bool relativeToleranceCompare(double x, double y)
{
    double maxXY = std::max( std::fabs(x) , std::fabs(y) ) ;
    return std::fabs(x - y) <= std::numeric_limits::epsilon()*maxXY ;
}

文章指出,绝对的测试失败时x和y又大又处于相对的情况下失败的时候都小.假设他的绝对和相对容差是相同的,组合测试将如下所示:

bool combinedToleranceCompare(double x, double y)
{
    double maxXYOne = std::max( { 1.0, std::fabs(x) , std::fabs(y) } ) ;

    return std::fabs(x - y) <= std::numeric_limits::epsilon()*maxXYOne ;
}

你写的代码被窃听:

return (diff < EPSILON) && (-diff > EPSILON);

正确的代码是:

return (diff < EPSILON) && (diff > -EPSILON);

(...是的,这是不同的)

我想知道在某些情况下,晶圆厂不会让你失去懒惰的评价.我会说这取决于编译器.你可能想尝试两者.如果它们的平均值相等,那就采用fabs实现.

如果你有一些关于两个浮点数哪个更可能比其他浮点数更大的信息,你可以按照比较的顺序进行游戏以更好地利用延迟评估.

最后,通过内联此函数可能会获得更好的结果.虽然不太可能改善...

编辑:OJ,感谢您更正代码.我相应地删除了我的评论

`return fabs(a - b)

这样可以:

输入的数量级没有太大变化

极少数相反的符号可以视为相等

但否则它会让你陷入困境.双精度数字的分辨率约为16位小数.如果您要比较的两个数字的幅度大于EPSILON*1.0E16,那么您可能会说:

return a==b;

我将研究一种不同的方法,假设您需要担心第一个问题,并假设第二个问题适合您的应用程序.一个解决方案是这样的:

#define VERYSMALL  (1.0E-150)
#define EPSILON    (1.0E-8)
bool AreSame(double a, double b)
{
    double absDiff = fabs(a - b);
    if (absDiff < VERYSMALL)
    {
        return true;
    }

    double maxAbs  = max(fabs(a) - fabs(b));
    return (absDiff/maxAbs) < EPSILON;
}

这在计算上是昂贵的,但它有时是所要求的.这是我们在公司必须做的事情,因为我们处理工程库,输入可能会有几十个数量级.

无论如何,关键在于(并且几乎适用于所有编程问题):评估您的需求,然后提出解决方案来满足您的需求 - 不要认为简单的答案将满足您的需求.如果在你的评估之后你发现fabs(a-b) < EPSILON它就足够了,完美 - 使用它!但要注意它的缺点和其他可能的解决方案.

我花了很长时间在这个伟大的线程中浏览材料.我怀疑每个人都想花这么多时间,所以我要强调我学到的内容和我实施的解决方案的总结.

快速摘要

float比较有两个问题:你的精度有限,"近零"的含义取决于上下文(见下一点).

1E-8与1E-16大致相同吗？如果您正在查看噪声传感器数据,那么可能是,但如果您正在进行分子模拟,则可能不是!结论:您总是需要在特定函数调用的上下文中考虑容差值,而不仅仅是使其成为通用的应用程序范围的硬编码常量.

对于通用库函数,使用具有默认容差的参数仍然很好.典型的选择numeric_limits::epsilon()与float.h中的FLT_EPSILON相同.然而,这是有问题的,因为epsilon用于比较1.0之类的值,如果与epsilon不相同,则用于像1E9这样的值.FLT_EPSILON定义为1.0.

检查数字是否在容差范围内的明显实现是fabs(a-b) <= epsilon不起作用的,因为默认epsilon定义为1.0.我们需要根据a和b来向上或向下扩展epsilon.

这个问题有两种解决方案:要么将epsilon设置为成比例,max(a,b)要么可以获得围绕a的下一个可表示数字,然后查看b是否属于该范围.前者称为"相对"方法,后来称为ULP方法.

当与0比较时,两种方法实际上都失败了.在这种情况下,应用程序必须提供正确的容差.

实用功能实现(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
static bool isApproximatelyEqual(TReal a, TReal b, TReal tolerance = std::numeric_limits::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
static bool isApproximatelyZero(TReal a, TReal tolerance = std::numeric_limits::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
static bool isDefinitelyLessThan(TReal a, TReal b, TReal tolerance = std::numeric_limits::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
static bool isDefinitelyGreaterThan(TReal a, TReal b, TReal tolerance = std::numeric_limits::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
static bool isWithinPrecisionInterval(TReal a, TReal b, unsigned int interval_size = 1)
{
    TReal min_a = a - (a - std::nextafter(a, std::numeric_limits::lowest())) * interval_size;
    TReal max_a = a + (std::nextafter(a, std::numeric_limits::max()) - a) * interval_size;

    return min_a <= b && max_a >= b;
}

正如其他人所指出的那样,使用固定指数epsilon(例如0.0000001)对于远离epsilon值的值将是无用的.例如,如果您的两个值是10000.000977和10000,则这两个数字之间没有 32位浮点值 - 10000和10000.000977尽可能接近,而不是逐位相同.这里,小于0.0009的ε是没有意义的; 你也可以使用直线相等运算符.

同样,当两个值的大小接近epsilon时,相对误差增加到100%.

因此,尝试将诸如0.00001的固定点数与浮点值(指数是任意的)混合是一种毫无意义的练习.只有在可以确保操作数值位于窄域(即接近某个特定指数)的情况下,以及为该特定测试正确选择epsilon值时,这才会起作用.如果你从空中拉出一个数字("嘿!0.00001很小,那一定是好的!"),你注定会出现数字错误.我花了很多时间来调试糟糕的数字代码,其中一些可怜的schmuck在随机epsilon值中抛出,以使另一个测试用例工作.

如果你进行任何类型的数值编程并且认为你需要达到定点epsilons,请阅读BRUCE关于比较浮点数的文章.

比较浮点数

Qt实现了两个功能，也许您可​​以从中学习：

static inline bool qFuzzyCompare(double p1, double p2)
{
    return (qAbs(p1 - p2) <= 0.000000000001 * qMin(qAbs(p1), qAbs(p2)));
}

static inline bool qFuzzyCompare(float p1, float p2)
{
    return (qAbs(p1 - p2) <= 0.00001f * qMin(qAbs(p1), qAbs(p2)));
}

您可能需要以下功能，因为

请注意，比较p1或p2为0.0的值将不起作用，比较其中一个值为NaN或无穷大的值也将无效。如果值之一始终为0.0，请改用qFuzzyIsNull。如果其中一个值很可能是0.0，则一种解决方法是将两个值加1.0。

static inline bool qFuzzyIsNull(double d)
{
    return qAbs(d) <= 0.000000000001;
}

static inline bool qFuzzyIsNull(float f)
{
    return qAbs(f) <= 0.00001f;
}