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A three-dimensional plot of an indicator function, shown over a square two-dimensional domain (set X): the 'raised' portion overlays those two-dimensional points which are members of the 'indicated' subset (A).
In mathematics, an indicator function or a characteristic function is a function defined on a setX that indicates membership of an element in a subsetA of X, having the value 1 for all elements of A and the value 0 for all elements of X not in A. It is usually denoted by a symbol 1 or I, sometimes in boldface or blackboard boldface, with a subscript specifying the subset.
In other contexts, such as computer science, this would more often be described as a boolean predicate function (to test set inclusion).
The indicator function of a subset A of a set X is a function
The Iverson bracket allows the equivalent notation, , to be used instead of .
The function is sometimes denoted , , KA or even just . (The Greek letter appears because it is the initial letter of the Greek word , which is the ultimate origin of the word characteristic.)
The set of all indicator functions on can be identified with , the power set of . Consequently, both sets are sometimes denoted by . This is a special case () of the notation for the set of all functions .
The term "characteristic function" has an unrelated meaning in classic probability theory. For this reason, traditional probabilists use the term indicator function for the function defined here almost exclusively, while mathematicians in other fields are more likely to use the term characteristic function to describe the function that indicates membership in a set.
In many cases, such as order theory, the inverse of the indicator function may be defined. This is commonly called the generalized Möbius function, as a generalization of the inverse of the indicator function in elementary number theory, the Möbius function. (See paragraph below about the use of the inverse in classical recursion theory.)
Mean, variance and covariance
Given a probability space with , the indicator random variable is defined by if otherwise
Characteristic function in recursion theory, Gödel's and Kleene's representing function
Kurt Gödel described the representing function in his 1934 paper "On Undecidable Propositions of Formal Mathematical Systems". (The paper appears on pp. 41-74 in Martin Davis ed. The Undecidable):
"There shall correspond to each class or relation R a representing function ?(x1, . . ., xn) = 0 if R(x1, . . ., xn) and ?(x1, . . ., xn) = 1 if ~R(x1, . . ., xn)." (p. 42; the "~" indicates logical inversion i.e. "NOT")
Stephen Kleene (1952) (p. 227) offers up the same definition in the context of the primitive recursive functions as a function ? of a predicate P takes on values 0 if the predicate is true and 1 if the predicate is false.
For example, because the product of characteristic functions ?1*?2* . . . *?n = 0 whenever any one of the functions equals 0, it plays the role of logical OR: IF ?1 = 0 OR ?2 = 0 OR . . . OR ?n = 0 THEN their product is 0. What appears to the modern reader as the representing function's logical inversion, i.e. the representing function is 0 when the function R is "true" or satisfied", plays a useful role in Kleene's definition of the logical functions OR, AND, and IMPLY (p. 228), the bounded- (p. 228) and unbounded- (p. 279ff) mu operators (Kleene (1952)) and the CASE function (p. 229).
Characteristic function in fuzzy set theory
In classical mathematics, characteristic functions of sets only take values 1 (members) or 0 (non-members). In fuzzy set theory, characteristic functions are generalized to take value in the real unit interval [0, 1], or more generally, in some algebra or structure (usually required to be at least a poset or lattice). Such generalized characteristic functions are more usually called membership functions, and the corresponding "sets" are called fuzzy sets. Fuzzy sets model the gradual change in the membership degree seen in many real-world predicates like "tall", "warm", etc.
The derivative of the Heaviside step function can be seen as the 'inward normal derivative' at the 'boundary' of the domain given by the positive half-line. In higher dimensions, the derivative naturally generalises to the inward normal derivative, while the Heaviside step function naturally generalises to the indicator function of some domain D. The surface of D will be denoted by S. Proceeding, it can be derived that the inward normal derivative of the indicator gives rise to a 'surface delta function', which can be indicated by ?S(x):
where n is the outward normal of the surface S. This 'surface delta function' has the following property: