diff --git a/docs/chapter_data_structure/number_encoding.md b/docs/chapter_data_structure/number_encoding.md index b5f81bbef..012eefbf8 100644 --- a/docs/chapter_data_structure/number_encoding.md +++ b/docs/chapter_data_structure/number_encoding.md @@ -135,7 +135,7 @@ $$ **尽管浮点数 `float` 扩展了取值范围，但其副作用是牺牲了精度**。整数类型 `int` 将全部 32 比特用于表示数字，数字是均匀分布的；而由于指数位的存在，浮点数 `float` 的数值越大，相邻两个数字之间的差值就会趋向越大。 -如下表所示，指数位 $E = 0$ 和 $E = 255$ 具有特殊含义，**用于表示零、无穷大、$\mathrm{NaN}$ 等**。 +如下表所示，指数位 $\mathrm{E} = 0$ 和 $\mathrm{E} = 255$ 具有特殊含义，**用于表示零、无穷大、$\mathrm{NaN}$ 等**。

表   指数位含义

diff --git a/en/docs/chapter_data_structure/number_encoding.md b/en/docs/chapter_data_structure/number_encoding.md index eb75cb6f2..c8b7366e3 100644 --- a/en/docs/chapter_data_structure/number_encoding.md +++ b/en/docs/chapter_data_structure/number_encoding.md @@ -135,7 +135,7 @@ Now we can answer the initial question: **The representation of `float` includes **However, the trade-off for `float`'s expanded range is a sacrifice in precision**. The integer type `int` uses all 32 bits to represent the number, with values evenly distributed; but due to the exponent bit, the larger the value of a `float`, the greater the difference between adjacent numbers. -As shown in the table below, exponent bits $E = 0$ and $E = 255$ have special meanings, **used to represent zero, infinity, $\mathrm{NaN}$, etc.** +As shown in the table below, exponent bits $\mathrm{E} = 0$ and $\mathrm{E} = 255$ have special meanings, **used to represent zero, infinity, $\mathrm{NaN}$, etc.**

Table   Meaning of exponent bits