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Update symbol format in LaTex of number encoding section (#1193)

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Nan Lei 2024-04-07 01:33:42 +08:00 committed by GitHub
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docs/chapter_data_structure/number_encoding.md
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@ -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**。
<p align="center"> 表 <id> &nbsp; 指数位含义 </p>

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en/docs/chapter_data_structure/number_encoding.md
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@ -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.**
<p align="center"> Table <id> &nbsp; Meaning of exponent bits </p>