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.
通过使用空间分片的方法可以为浮点精度问题提供了一个解决途径。
The spatial lamination method proposed in this paper proves to be an effective way to solve the problem with high speed, good hardware compatibility and adjustable property.
更常用的同义词是FLOAT类型，它拥有任意的浮点精度，可以在声明数据类型时指定，例如float (val)。
A more general synonym is the float type, which has an arbitrary floating-point precision that you specify when declaring the data type as float (val).
如果从未遇到过准确精度数据类型，那么对算术类型和浮点类型可能有些迷惑。
If you've never encountered an exact precision data type, the distinction between a decimal and floating-point type may be confusing.
“普通消费者不需要双精度(浮点数支持)”他说。
Microwulf经测试，运行能力为每秒26.25gigaflops，相当于每秒能进行260多亿次的双精度浮点运算。
Microwulf has been measured to process 26.25 gigaflops, or 26.25 billion double-precision floating point instructions, per second.
使用float的惟一时机就是操纵精度有限的大型多维浮点数字数组，此时存储空间较为重要。
The only time floats are used is in manipulation of large multidimensional arrays of floating-point numbers with limited precision in which the storage space would be significant.
对于财务应用程序(尤其是需要精确到最后一位的会计应用程序)，处理浮点数和双精度数时也需要格外小心。
Financial applications (and especially accounting applications that require accuracy to the last cent) also need to be exceedingly careful when manipulating floats and doubles.
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.
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.
由有限精度浮点数字引起的很小的舍入错误就会严重歪曲数学精度计算。
Mathematically precise calculations can be thrown severely askew by small round-off errors caused by finite-precision floating-point Numbers.
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.
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。
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.
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.
除支持基本的字符串之外，您还可以存储布尔值、双精度数、浮点数、整型数、长整型数和字节数组(考虑序列化)。
In addition to basic string support, you can store booleans, doubles, floats, integers, longs, and byte arrays (think serialization).
Regan推测这个特殊数字的麻烦之处在于它是“最大的次法线双精度浮点数”。
Regan speculated that this particular number is troublesome because it is the "largest subnormal double-precision floating-point number."
指数函数是一个很好的例子，它表明处理有限精度浮点数(而不是无限精度实数)时是需要非常小心的。
The exponential function serves as a good example of how careful you have to be when dealing with limited-precision floating-point Numbers instead of infinitely precise real numbers. ex (Math.exp ).
其中一个构造函数以双精度浮点数作为输入，另一个以整数和换算因子作为输入，还有一个以小数的String表示作为输入。
One takes a double-precision floating point as input, another takes an integer and a scale factor, and another takes a String representation of a decimal number.
通常支持的类型有整数、Boolean、ASCII字符串、双精度带符号浮点数、日期时间、集合、列表、属性。
In general supported types are Integer, Boolean, ASCII string, double-precision signed floating point number, date-time, set, list, properties.
Derby对实数提供了多种格式的支持：单精度浮点、双精度浮点以及准确的算术表示，如表2所示。
Derby provides support for real Numbers in several formats: single-precision floating-point, double-precision floating-point, and an exact representation decimal, as presented in Table 2.
比如0.1不能被表示为二进制数，所以被四舍五入储存为双精度浮点数。
For example, 0.1 cannot be represented binary, and therefore is rounded when stored in a double. Let me show you using python.
编写自定义着色器时，您应该始终指定浮点变量的精度。
You should always specify the precision of floating point variables when writing custom shaders.
浮点数和双精度数字的二进制表示。
如果参数为NaN或Inf，则对应的浮点数和双精度数的结果分别是128和1024。
If the argument is NaN or Inf, then the result will be 128 for a float and 1024 for a double.
2比特的浮点型数据精度更大。
spu_mul处理浮点乘法(单精度和多精度)。
Spu_mul handles floating point multiplication (single and double precision).
浮点比较的问题在于精度 —f1和result*result在小数点后面的几位不一致。
The issue with floating point comparisons is that of precisions—f1 and result*result start differing from a couple of places after the decimal point.
我不相信。通过阅读维基一次。单精度浮点数似乎可以提供6 - 9精度。
I am not convinced. By reading the Wiki again. It seems the single-precision float can provide 6-9 precision.
写自定义的着色器时，应该指定浮点数精度。
You should always specify the precision of floating point variables when writing custom shaders.
采用块浮点算法以提高动态范围和运算精度。
Block floating-point arithmetic is used to enhance the dynamic range and computation accuracy.
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