PHP：浮点数学和比较 [英] PHP: float maths and comparisons

问题描述

我希望所有下面的比较是bool（true），但他们不是。
任何人都可以解释这个问题吗？

I would expect all below comparisons to be bool (true) but they are not. Can anyone explain this?

<?php

$f = 12;
$f += 5.95;
$f += 5.95;
$f += 5.95;

echo 'var_dump($f) = ';
var_dump($f);

echo 'var_dump($f == \'29.85\') = ';
var_dump($f == '29.85');

echo 'var_dump($f == 29.85) = ';
var_dump($f == 29.85);

echo 'var_dump($f == (float)\'29.85\') = ';
var_dump($f == (float)'29.85');

echo 'var_dump($f == \'29.85\') = ';
var_dump((string)$f == '29.85');

echo 'var_dump(round($f, 2) == \'29.85\') = ';
var_dump(round($f, 2) == '29.85');

$ php test.php

$ php test.php

var_dump($f) = float(29.85)
var_dump($f == '29.85') = bool(false)
var_dump($f == 29.85) = bool(false)
var_dump($f == (float)'29.85') = bool(false)
var_dump($f == '29.85') = bool(true)
var_dump(round($f, 2) == '29.85') = bool(true)

$ php -v

PHP 5.2.14 (cli) (built: Jul 23 2010 15:23:00)
Copyright (c) 1997-2010 The PHP Group
Zend Engine v2.2.0, Copyright (c) 1998-2010 Zend Technologies
    with Xdebug v2.1.0, Copyright (c) 2002-2010, by Derick Rethans

推荐答案

浮点数精确。虽然它取决于
系统，但是PHP通常使用IEEE 754双精度格式
，由于按照1.11e-16的顺序
舍入，将给出最大相对误差。非基本算术运算可能给出更大的
错误，当然，当
的几个操作复合时，必须考虑错误处理。

Floating point numbers have limited precision. Although it depends on the system, PHP typically uses the IEEE 754 double precision format, which will give a maximum relative error due to rounding in the order of 1.11e-16. Non elementary arithmetic operations may give larger errors, and, of course, error progragation must be considered when several operations are compounded.

有理数可以精确表示为
基数10中的浮点数，如0.1或0.7，没有
精确表示为基数2中的浮点数，这是内部使用的
，无论尾数的大小。因此，它们
不能被转换为它们的内部二进制对等体而没有
小的精度损失。这可能导致混乱的结果：对于
示例，floor（（0.1 + 0.7）* 10）通常将返回7而不是
预期8，因为内部表示将像
7.9999999999999991118 ....

Additionally, rational numbers that are exactly representable as floating point numbers in base 10, like 0.1 or 0.7, do not have an exact representation as floating point numbers in base 2, which is used internally, no matter the size of the mantissa. Hence, they cannot be converted into their internal binary counterparts without a small loss of precision. This can lead to confusing results: for example, floor((0.1+0.7)*10) will usually return 7 instead of the expected 8, since the internal representation will be something like 7.9999999999999991118....

因此，不要将浮点数结果与最后一位数相关联，永远不要
比较浮点数是否相等。

So never trust floating number results to the last digit, and never compare floating point numbers for equality.

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