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In computing, NaN (/ n æ n /), standing for Not a Number, is a particular value of a numeric data type (often a floating-point number) which is undefined as a number, such as the result of 0/0. Systematic use of NaNs was introduced by the IEEE 754 floating-point standard in 1985, along with the representation of other non-finite quantities ...
An example is explicit optimization of a code path which is considered a bottleneck by the profiler. In the case of Common Lisp, this is possible by using an explicit declaration to type-annotate a variable to a machine-size word (fixnum) [15] and lower the type safety level to zero [16] for a particular code block. [17] [18] [19] [20]
Signalling 'Not a Number', the sign bit is meaningless. 11: 0: Floating-point Indefinite, the result of invalid calculations such as square root of a negative number, logarithm of a negative number, 0 / 0 , infinity / infinity , infinity times 0, and others, when the processor has been configured to not generate exceptions for ...
Some programming languages such as Lisp, Python, Perl, Haskell, Ruby and Raku use, or have an option to use, arbitrary-precision numbers for all integer arithmetic. Although this reduces performance, it eliminates the possibility of incorrect results (or exceptions) due to simple overflow.
The 10-bit format has a 5-bit mantissa, and the 11-bit format has a 6-bit mantissa. [8] [9] IEEE SA Working Group P3109 is currently working on a standard for 8-bit minifloats optimized for machine learning. The current draft defines not one format, but a family of 7 different formats, named "binary8pP", where "P" is a number from 1 to 7.
Magic numbers become particularly confusing when the same number is used for different purposes in one section of code. It is easier to alter the value of the number, as it is not duplicated. Changing the value of a magic number is error-prone, because the same value is often used several times in different places within a program. [6]
Therefore, the smallest number in the normalized range is narrower than double precision. The smallest number with full precision is 1000...0 2 (106 zeros) × 2 −1074, or 1.000...0 2 (106 zeros) × 2 −968. Numbers whose magnitude is smaller than 2 −1021 will not have additional precision compared with double precision.
Convolutional code with any code rate can be designed based on polynomial selection; [15] however, in practice, a puncturing procedure is often used to achieve the required code rate. Puncturing is a technique used to make a m/n rate code from a "basic" low-rate (e.g., 1/n) code. It is achieved by deleting of some bits in the encoder output.