浮点或者双精度浮点的内存空间是限定了的，所以某些值不能被表示。
The memory space of a float or double value is limited, so some values cannot be represented.
如果我们回想一下我们一开始讲过的数据对象的种类，浮点数，整数，字符串，它们实际上有相同的行为方式。
If we go back up to the kinds of data objects we started with, floats, ints, strings, they actually behave the same way.
如果任意一个操作数是浮点型的，则所有操作数都将按浮点型进行计算，并且结果也是浮点型的。
If any of the operands is a float, then all operands are evaluated as floats, and the results are floats.
这些类允许各种几何图形建立在双精度或浮点精度的坐标系上。
These classes allow the various geometric shapes to be constructed with coordinates of either double or float precision.
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。
It had separate floating point registers and could scale from the low - to the high-end workstations.
然后会为任何可选的浮点单元(FPU)检测CPU的类型，并将其存储起来供以后使用。
The type of CPU is detected along with any optional floating-point unit (FPU) and stored away for later use.
如果您读取的浮点数，还有另外需要关注的问题。
If you're reading a floating point number, there are other concerns.
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。
Those topics deal with floating point and vector processing and are outside the scope of this article.
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。
Operations for floating point variables are limited to simple assignment expressions and as arguments to VUE functions.
这些工作负荷会占用大量的浮点单元或内存带宽。
These workloads heavily use either the floating-point units or the memory bandwidth.
您可以读取被定义为“标量”的整型、浮点型和字符串值。
You can read integer, floating point, and string values that are all defined as' scalar 'objects.
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。
However, note that the payload looks very strange for a payload that simply returns a floating point number.
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。
An attempt was made to execute a floating point instruction when the floating point available bit in the MSR (machine status register) was disabled.
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。
Other ranges can be used, but in my experience floating point numbers work best.
注意浮点乘法没有这样的问题。
Note that floating-point multiplication doesn't have these issues.
确认了发送数据的传感器之后，将使用方法第二个参数传递的浮点值数组中所包含的数据更新相应的ui元素。
Once the sending sensor is identified, the appropriate UI elements are updated with data contained in the array of float values passed as the second argument to the method.
浮点运算很少是精确的。
随着数字变大，它们之间的浮点数就会越来越少。
支持的变量包括整型、浮点型的数字、字符串、数组和对象。
Supported variables include integers, floating point Numbers, strings, arrays, and objects.
当然，结果不一定是有意义的 —— 比如字符串和浮点数的比较就没有意义。
Sure, the result would not necessarily be meaningful -- a string is neither objectively less than nor greater than a float.
这不是最好的办法但它确实有用,我可以得到底的值的类型然后,和一个真的浮点数的类型比比,看他们是不是一样？
Now, this is not the nicest way to do it but it'll work. I can look at the type of the value of base and compare it to the type of an actual float and see, are they the same?
在 64位系统上，整型被转换成 64 位的整型值，单精度的浮点类型被转换成双精度的浮点类型。
On a 64-bit system, integral types are converted to 64-bit integral types, and single precision floating point types are promoted to double precision.
它只是返回一个表示温度的浮点数。
All it does is return a floating point number representing the temperature.
如果参数为0，则对应的浮点数和双精度数的结果分别是- 127和- 1023。
If the argument is zero, then the result will be -127 for a float and -1023 for a double.
程序期望查找ascii格式的单精度浮点数；任何无关的字符都将忽略。
The program expects to find a single floating-point number in ASCII format; any extraneous characters will be ignored.
对于X和Y维，我们只有用于两个浮点值的8个字节。
There we have merely 8 bytes for the two floating point values for the X and Y dimension.
ieee浮点值的格式如图1所示。
The layout of IEEE floating point values is shown in Figure 1.
signed/unsigned关键字是声明非浮点类型必需的。
The signed/unsigned keyword is required for non-floating point declarations.
基本浮点类型和包装类浮点有不同的比较行为。
Primitive float type and wrapper class float have different comparison behavior.
基本浮点类型和包装类浮点有不同的比较行为。
Primitive float type and wrapper class float have different comparison behavior.
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