C＃通过简单的数学运算获得奇怪的结果 [英] C# Getting strange results for a simple math operations

问题描述

我注意到在添加两个双打时有一些奇怪的行为，有时它工作正确，有时却不正确！

I have noticed for some strange behavior while adding two doubles, sometimes it works correct, and sometimes not!

这里是第一个示例：

double num1 = 0.1 + 0.7; //Guess the result?

简单-0.8 !!!

Easy - 0.8 !!! or not?

看看奇怪的结果：

然后猜测，if语句在else块中并打印 num1 -不，它不打印0.799999999999993，它打印0.8。

And guess what, the if statement is going inside the else block, and prints the num1 - and no, it doesn't prints 0.799999999999993, it prints 0.8.

所以我走了进一步前进并尝试以下代码：

So I have gone one more step forward and tried this code:

if (0.1 + 0.7 == 0.8) //Returns false ??
{
    Console.WriteLine("Correct");
}

好的，很奇怪，但是现在我找到了正确的方法，应该使用f （浮动）。我记得double有很多空格，所以它可以包含更大的数字，也许是原因。

OK, strange, but now I found the correct way, it should use f (float). As I remember double has many spaces, so it can contain higher numbers, maybe this is the cause.

float num2 = 0.1f + 0.7f;

if (num2 == 0.8f) //Perfect - finally works !!!
{
    Console.WriteLine("Correct");
}
else
{
    Console.WriteLine(num2);
}

但是现在，我尝试这样做-并再次返回false，为什么？

But now, I try this - and again it returns false, why?

if (0.1f + 0.7f == 0.8f) //Returns false :(
{
    Console.WriteLine("Correct");
}

调试时手表的结果：

有人可以向我解释这里出什么问题吗？是那些错误吗？

Can someone explain me what's wrong here? Are those bugs?

预先感谢。

推荐答案

浮点运算并不精确，您应该阅读文章每位计算机科学家应了解的浮点运算法则。如果您希望计算精确到十进制，则应使用 十进制 类型，其中 0.1m + 0.7m == 0.8m 是 true 。您应注意，十进制使用浮点数，就像 float 和 double ，但底数是10，而不是2。由于以2为底数的处理非常容易且经过高度优化，因此十进制要慢得多。它也有其自身的不准确性，仅在处理可以用短小数位数表示的数字（例如 0.7 和 0.1 ），使其更适合财务数据。

Floating point arithmetic is not precise. You should read the article What Every Computer Scientist Should Know About Floating-Point Arithmetic. If you want your calculations to be precise in a decimal sense, you should use the decimal type, where 0.1m + 0.7m == 0.8m is true. You should note that decimal uses floating point numbers just like float and double, except that the base is 10, not 2. Since dealing with base 2 is so much easier and highly-optimized, decimal is much slower. It also has its own inaccuracies, it's only precise when dealing with numbers that can be represented in a short number of decimal digits (like 0.7 and 0.1), making it good for financial data.

了解浮点数的另一篇有用的文章是维基百科的双精度浮点格式。

Another useful article to understand floating point numbers is Wikipedia's Double-precision floating-point format.

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