Don't use floating point Numbers for exact values.
不要用浮点值表示精确值。
Why fabs giving weird result while comparing floating point Numbers?
为什么工厂给怪异的结果在比较浮点数？
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。
Floating point Numbers can reach much higher values but their precision not.
浮点数可以达到更高的值，但其精度。
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。
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。
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。
The fundamental types are characters, and integers and floating point Numbers of several sizes.
其基本类型为几种大小的字符、整数和浮点数类型。
The non-ordered nature of NaN adds further opportunities for error when comparing floating point numbers.
NaN的无序性质使得在比较浮点数时更容易发生错误。
Floating point Numbers are best reserved for values such as measurements, whose values are fundamentally inexact to begin with.
浮点数最好用来表示象测量值这类数值，这类值从一开始就不怎么精确。
In Part 2, I'll explore the functions more designed for operating on floating point Numbers as opposed to abstract real Numbers.
在第2部分，我将探讨专为操作浮点数(与抽象实数相反)而设计的函数。
A secure pseudorandom bit sequence generator is designed and the bit sequences are then transformed into floating point Numbers.
设计了一种密码安全的伪随机比特序列发生器，再将比特序列转化为浮点数序列。
Color changes are another common alteration. Values for color are floating point Numbers from 0 to 1, 0 being black, 1 being white.
修改颜色是另一个常见的操作。颜色值是一个从0到1的浮点数，0代表黑色，1代表白色。
HTTP content negotiation uses short floating point numbers to indicate the relative importance, or weight, of various negotiable parameters.
HTTP内容协商使用短浮点数来表示各种可协商参数的相对重要性（或权重）。
This article proposes a quick sort method based on the machine code of floating point numbers, which can sort the floating point numbers distributed at random.
本文提出一种可对任意分布的浮点数进行排序的快速排序方法，它基于浮点数的机内编码，具有速度快、实现简单、实用的特点。
So as well as being able to pattern match over values, such as integers, floating point Numbers and strings, we are also able to pattern match over an objects type.
所以正如能够匹配很多值一样，我们也能够匹配很多对象类型，如：整数、浮点数和字符串。
You can run into issues of things like overflow, underflow, with floating point numbers and when you see a whole bunches of ones, it's particularly a good time to be suspicious.
来看看哪儿会出问题，你可能会碰到浮点数中的溢出和下溢问题,当你碰到一系列这种问题后，可能就会适时的开始怀疑结果的正确性。
The main thing to understand is that Psyco is useful for handling blocks that loop many times, and it knows how to optimize operations involving integers and floating point numbers.
主要是要明白Psyco对于处理多次循环的块是很有用的，而且它知道如何优化涉及整数和浮点数的操作。
Because of the special behavior of infinity, NaN, and 0, certain transformations and optimizations that may appear harmless are actually incorrect when applied to floating point Numbers.
由于无穷大、NaN和0的特殊行为，当应用浮点数时，可能看似无害的转换和优化实际上是不正确的。
The floating point numbers operation modularization deals with data through floating point numbers program of format, multiplication and division, which improves operation precision of the data;
浮点数运算模块是采用浮点数格式化等子程序对数据进行处理，提高了数据的运算精度；
As an example we will write a small program to read and evaluate arithmetic expressions consisting of floating point Numbers, parentheses and the usual operators for addition, subtraction, and so on.
作为例子，我们将要写一个计算由浮点数、圆括号及一些常用的加、减等算术符号组成的算术表达式的程序。
As an example we will write a small program to read and evaluate arithmetic expressions consisting of floating point Numbers, parentheses and the usual operators for addition, subtraction, and so on.
例如我们要写一个计算由浮点数、圆括号及一些常用的加、减等算术符号组成的算术表达式的程序。
Floating point and decimal Numbers are not nearly as well-behaved as integers, and you cannot assume that floating point calculations that "should" have integer or exact results actually do.
浮点数和小数不象整数一样“循规蹈矩”，不能假定浮点计算一定产生整型或精确的结果，虽然它们的确“应该”那样做。
For floating-point Numbers, you place a decimal as the separator between the whole part of the number and the fraction.
对于浮点数，您将在整数部分和小数部分之间放置小数点。
Here in Part 2, I focus on the functions that make sense only when you realize that they're designed for operating on floating-point Numbers instead of abstract real Numbers.
在第2部分中，我主要关注这样一些函数，它们的目的是操作浮点数，而不是抽象实数。
So the arguments (the data) — whether they are floating-point Numbers, integers, character strings, or complex objects — are encoded into a format that can be transferred to the RPC receiver.
所以自变量(数据)-无论是浮点数、整数、字符串还是复杂对象-都要编码成可以传输到RPC接收方的格式。
There are differences in the definition of floating-point Numbers and long variable types.
浮点型数字和长变量类型的定义是有差异的。
Mathematically precise calculations can be thrown severely askew by small round-off errors caused by finite-precision floating-point Numbers.
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。
In most programming languages, Numbers that include a decimal fraction are called floating-point Numbers, which are used to approximate real Numbers in mathematics.
在大部分的编程语言中，包含了小数点的数字称之为浮点数，可以用它来表示数学中的实数。
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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