铸造是否具有破坏性？

问题描述

在PHP中，我知道我们不应该在没有像bcmath这样的东西的浮动体上进行数学，而是仅仅是一个用于浮动破坏性的字符串的方法。

表达式如（float）'5.111'=='5.111'，始终为true？或者演员本身会将其更改为像$ code> 5.1110000000000199837 一样的数字转换？

主要原因是，正如我使用（int）来转义进入数据库的整数值，I想要以相同的方式使用（float），而不必依赖引号和我的转义函数。

解决方案

不，铸造成浮动几乎总是破坏性。

在你的例子中，以二进制表示的5.111是：

  101.00011100011010100111111011111001110110110010001011010000111001 ... 
  

浮点数将存储23位数：

  101.0001110001101010011 
（5.1109981536865234375）
  

双重将存储52位数：

  101.0001110001101010011111101111100111011011001000101 
（5.1109999999999988773424774990417063236236572265625）
  

$ b $在这种情况下，没有什么区别。然而，在更大的数量，它可以影响你显示的内容。

例如：

1025.4995

double：

  10000000001.011111111101111100111011011001000101101 
（1025.499499999999898136593401432037353515625）
  

float：

  10000000001.011111111101 
 （1025.499267578125）
  

您可以看到在大约8位数后，精度开始急剧下降。 >

双人将轮到 1025.4995 ，而float将为 1025.4993

In PHP, I know we shouldn't do math on floats without things like bcmath, but is the mere act of casting a string to float destructive?

Will expressions like (float)'5.111' == '5.111', always be true? Or will the cast itself change that to something like 5.1110000000000199837 as the number is converted?

The main reason is, just as I use (int) to escape integer values going into a database, I would like to use (float) in the same way, without having to rely on quotes and my escape function.

解决方案

NO, Casting to a float is almost always destructive.

In your example, 5.111 represented in binary is:

101.00011100011010100111111011111001110110110010001011010000111001...

A float would store 23 digits:

101.0001110001101010011
(5.1109981536865234375)

A double would store 52 digits:

101.0001110001101010011111101111100111011011001000101
(5.1109999999999988773424774990417063236236572265625)

In this case, there wouldn't be a difference. However, in larger numbers, it can affect what you display.

For example:

1025.4995

double:

10000000001.011111111101111100111011011001000101101
(1025.499499999999898136593401432037353515625)

float:

10000000001.011111111101
(1025.499267578125)

You can see the precision starts to drop off dramatically after around 8 digits.

The double would round to 1025.4995 whereas the float would be 1025.4993

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