• Float: a floating point number.

    浮点数:一个浮点数。

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  • Floating point arithmetic is rarely exact.

    浮点运算很少是精确的。

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  • EBNF-style description of floating point number.

    ebnf样式的浮点数描述。

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  • Don't use floating point Numbers for exact values.

    不要用浮点值表示精确值。

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  • It lacked floating point and parallel processing ability.

    它缺少浮点和并行处理功能。

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  • The number is in seconds but may be a floating point value.

    刷新时间的单位为秒,但可能是浮点值。

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  • The layout of IEEE floating point values is shown in Figure 1.

    ieee浮点值的格式如图1所示。

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  • Why do you need separate macros for floating point comparisons?

    为什么需要用单独的宏进行浮点数比较?

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  • If you're reading a floating point number, there are other concerns.

    如果您读取的浮点数,还有另外需要关注的问题。

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  • It would be best to try to avoid floating point comparison entirely.

    最好完全避免使用浮点数比较。

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  • The fitness formula should return a floating point number between 0 and 1.

    适应性公式返回的是介于0和1之间的一个浮点数。

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  • All it does is return a floating point number representing the temperature.

    它只是返回一个表示温度的浮点数。

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  • In future articles, I plan to take a closer look at floating point workloads.

    在未来的文章中,我计划仔细研究一下浮点工作负载。

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  • Other ranges can be used, but in my experience floating point numbers work best.

    您也可以使用其它的范围的数,但是我的经验告诉我,浮点数是最有效的。

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  • One of the trickiest checks in regression setups is doing floating point comparisons.

    回归测试中最棘手的检查之一是浮点比较。

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  • When the kernel is executing floating point instructions, the FPU state is not saved.

    当内核在执行浮点指令时,FPU状态不被保存。

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  • Floating point Numbers are not exact, and manipulating them will result in rounding errors.

    浮点数不是精确值,所以使用它们会导致舍入误差。

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  • Supported variables include integers, floating point Numbers, strings, arrays, and objects.

    支持的变量包括整型、浮点型的数字、字符串、数组和对象。

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  • IEEE 754 represents floating point Numbers as base 2 decimal Numbers in scientific notation.

    IEEE 754用科学记数法以底数为2的小数来表示浮点数。

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  • You can read integer, floating point, and string values that are all defined as' scalar 'objects.

    您可以读取被定义为“标量”的整型、浮点型和字符串值。

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  • It had separate floating point registers and could scale from the low - to the high-end workstations.

    它有单独的浮点寄存器,可以从低端工作站扩展到高端工作站。

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  • Those topics deal with floating point and vector processing and are outside the scope of this article.

    这些主题涉及的是浮点和向量处理,已经超出了本文的范围。

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  • They support Numbers of different types (integers and floating point), characters, strings, and so on.

    它们支持许多不同的类型(整型和浮点型)、字符、字符串等等。

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  • However, note that the payload looks very strange for a payload that simply returns a floating point number.

    但请注意,这个有效负载看起来非常奇怪,不象是只返回一个浮点数的有效负载。

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  • Floating point is a very similar concept, except that computers use binary rather than decimal as their base.

    浮点是一个非常类似的概念,除了计算机使用二进制而不是十进制作为基础。

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  • So both of these stories involve floating point values, but only in this case am I actually allocating memory.

    所以这两个故事都涉及到浮点类型,但是只有那样我们才能真正地分配到内存。

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  • Operations for floating point variables are limited to simple assignment expressions and as arguments to VUE functions.

    对于浮点变量的操作只限于简单的赋值表达式和作为VUE函数的变量。

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  • An attempt was made to execute a floating point instruction when the floating point available bit in the MSR (machine status register) was disabled.

    如果在MSR(机器状态寄存器)中可用的浮点位被禁用,将尝试执行一个浮点指令。

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  • These are based on the IEEE 754 standard, which defines a binary standard for 32-bit floating point and 64-bit double precision floating point binary-decimal Numbers.

    它们都依据IEEE 754标准,该标准为32位浮点和64位双精度浮点二进制小数定义了二进制标准。

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  • The standard mathematical operators, +, -, /, * are supported on both integer and floating point values, and you can mix and match floating-point and integers in calculations.

    在整数和浮点数上,都支持使用标准的数学操作符(+、-、/ 和 *),可以在算式中混合使用浮点数和整数。

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