上面的高分答案完全正确。

首先，你的问题中混合了以2为底和以10为底的数，然后当你把一个不能整除的数放在右边时，你就有问题了。比如十进制的1/3因为3不能整除10的幂，或者二进制的1/5不能整除2的幂。

Another comment though NEVER use equal with floating point numbers, period. Even if it is an exact representation there are some numbers in some floating point systems that can be accurately represented in more than one way (IEEE is bad about this, it is a horrible floating point spec to start with, so expect headaches). No different here 1/3 is not EQUAL to the number on your calculator 0.3333333, no matter how many 3's there are to the right of the decimal point. It is or can be close enough but is not equal. so you would expect something like 2*1/3 to not equal 2/3 depending on the rounding. Never use equal with floating point.

问题是你并不知道这个数字是否真的是61.0。考虑一下:

浮动a = 60; 浮动b = 0.1; c = a + b * 10;

c的值是多少?它不是61，因为b不是。1因为。1不是精确的二进制表示。

你不能用二进制精确地表示0.1，就像你不能用传统的英国尺测量0.1英寸一样。

英国的尺子，就像二进制分数一样，都是关于一半的。你可以测量半英寸，或四分之一英寸(当然是一半)，或八分之一，或十六分之一，等等。

If you want to measure a tenth of an inch, though, you're out of luck. It's less than an eighth of an inch, but more than a sixteenth. If you try to get more exact, you find that it's a little more than 3/32, but a little less than 7/64. I've never seen an actual ruler that had gradations finer than 64ths, but if you do the math, you'll find that 1/10 is less than 13/128, and it's more than 25/256, and it's more than 51/512. You can keep going finer and finer, to 1024ths and 2048ths and 4096ths and 8192nds, but you will never find an exact marking, even on an infinitely-fine base-2 ruler, that exactly corresponds to 1/10, or 0.1.

不过，你会发现一些有趣的事情。让我们看看我列出的所有近似值，对于每一个近似值，明确地记录0.1是大是小:

现在，如果向下读最后一列，就会得到0001100110011。1/10的无限重复二进制分数是0.0001100110011，这不是巧合……

一个简单的答案是:计算机没有无限的内存来存储分数(在以科学记数法的形式表示十进制数之后)。根据IEEE 754双精度浮点数标准，我们只有53位的限制来存储分数。 欲了解更多信息:http://mathcenter.oxford.emory.edu/site/cs170/ieee754/

我不想重复其他20个答案的总结，所以我只简单地回答:

答案在你的内容中:

为什么以两为基数的数字不能精确地表示一定的比率?

出于同样的原因，小数不足以表示某些比率，即分母中包含除2或5之外的素数因子的不可约分数，至少在其小数展开的尾数中总是有一个不确定的字符串。

为什么十进制数不能精确地用二进制表示?

This question at face value is based on a misconception regarding values themselves. No number system is sufficient to represent any quantity or ratio in a manner that the thing itself tells you that it is both a quantity, and at the same time also gives the interpretation in and of itself about the intrinsic value of the representation. As such, all quantitative representations, and models in general, are symbolic and can only be understood a posteriori, namely, after one has been taught how to read and interpret these numbers.

由于模型是主观的东西，在反映现实的范围内是正确的，我们不需要严格地将二进制字符串解释为2的负幂和正幂的和。相反，我们可以观察到，我们可以创建一组任意的符号，这些符号以2为基底或任何其他基底来精确地表示任何数字或比例。只要考虑一下，我们可以用一个词甚至一个符号来指代无穷大，而不需要“显示无穷大”本身。

As an example, I am designing a binary encoding for mixed numbers so that I can have more precision and accuracy than an IEEE 754 float. At the time of writing this, the idea is to have a sign bit, a reciprocal bit, a certain number of bits for a scalar to determine how much to "magnify" the fractional portion, and then the remaining bits are divided evenly between the integer portion of a mixed number, and the latter a fixed-point number which, if the reciprocal bit is set, should be interpreted as one divided by that number. This has the benefit of allowing me to represent numbers with infinite decimal expansions by using their reciprocals which do have terminating decimal expansions, or alternatively, as a fraction directly, potentially as an approximation, depending on my needs.

如果你用浮点数做一个足够大的数(它可以做指数)，那么小数点前也会不精确。所以我不认为你的问题是完全正确的，因为前提是错误的;移位10并不总是会产生更高的精度，因为在某些情况下，浮点数将不得不使用指数来表示数字的大小，这样也会失去一些精度。