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?
你写的代码有bug:
return (diff < EPSILON) && (-diff > EPSILON);
正确的代码应该是:
return (diff < EPSILON) && (diff > -EPSILON);
(…是的,这是不同的)
我想知道晶圆厂是否会让你在某些情况下失去懒惰的评价。我会说这取决于编译器。你可能想两种都试试。如果它们在平均水平上是相等的,则采用晶圆厂实现。
如果你有一些关于两个浮点数中哪一个比另一个更大的信息,你可以根据比较的顺序来更好地利用惰性求值。
最后,通过内联这个函数可能会得到更好的结果。不过不太可能有太大改善……
编辑:OJ,谢谢你纠正你的代码。我相应地删除了我的评论
General-purpose comparison of floating-point numbers is generally meaningless. How to compare really depends on a problem at hand. In many problems, numbers are sufficiently discretized to allow comparing them within a given tolerance. Unfortunately, there are just as many problems, where such trick doesn't really work. For one example, consider working with a Heaviside (step) function of a number in question (digital stock options come to mind) when your observations are very close to the barrier. Performing tolerance-based comparison wouldn't do much good, as it would effectively shift the issue from the original barrier to two new ones. Again, there is no general-purpose solution for such problems and the particular solution might require going as far as changing the numerical method in order to achieve stability.