Nan

on an electronic weather forecast In computing, NaN ( ), 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 \frac{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 such as infinities. In mathematics, the result of \frac{0}{0} is typically not defined as a number and may therefore be represented by NaN in computing systems. The square root of a negative number is not a real number, and is therefore also represented by NaN in compliant computing systems. NaNs may also be used to represent missing values in computations. Two separate kinds of NaNs are provided, termed quiet NaNs and signaling NaNs. Quiet NaNs are used to propagate errors resulting from invalid operations or values. Signaling NaNs can support advanced features such as mixing numerical and symbolic computation or other extensions to basic floating-point arithmetic.

on an electronic weather forecast In computing, NaN ( ), 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 \frac{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 such as infinities. In mathematics, the result of \frac{0}{0} is typically not defined as a number and may therefore be represented by NaN in computing systems. The square root of a negative number is not a real number, and is therefore also represented by NaN in compliant computing systems. NaNs may also be used to represent missing values in computations. Two separate kinds of NaNs are provided, termed quiet NaNs and signaling NaNs. Quiet NaNs are used to propagate errors resulting from invalid operations or values. Signaling NaNs can support advanced features such as mixing numerical and symbolic computation or other extensions to basic floating-point arithmetic. As specified, the predicates associated with the , \le , = , \ge , > mathematical symbols (or equivalent notation in programming languages) return false on an unordered relation. So, for instance, is not logically equivalent to x \ge y on unordered, i.e. when x or y is NaN, the former returns true while the latter returns false. However, \ne is defined as the negation of = , thus it returns true on unordered. From these rules, comparing x with itself, or , can be used to test whether x is NaN or non-NaN. The comparison predicates are either signaling or non-signaling on quiet NaN operands; the signaling versions signal the invalid-operation exception for such comparisons (i.e., by default, this just sets the corresponding status flag in addition to the behavior of the non-signaling versions). The equality and inequality predicates are non-signaling. The other standard comparison predicates associated with the above mathematical symbols are all signaling if they receive a NaN operand. The standard also provides non-signaling versions of these other predicates. The predicate isNaN( x ) determines whether a value is a NaN and never signals an exception, even if x is a signaling NaN. The IEEE floating-point standard requires that NaN \ne NaN hold. In contrast, the 2022 private standard of posit arithmetic has a similar concept, NaR (Not a Real), where NaR = NaR holds. > perl -mMath::BigInt -e "print Math::BigInt->new('foo')" NaN

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