Английская Википедия:Indicator function
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In mathematics, an indicator function or a characteristic function of a subset of a set is a function that maps elements of the subset to one, and all other elements to zero. That is, if Шаблон:Mvar is a subset of some set Шаблон:Mvar, then <math>\mathbf{1}_{A}(x)=1</math> if <math>x\in A,</math> and <math>\mathbf{1}_{A}(x)=0</math> otherwise, where <math>\mathbf{1}_A</math> is a common notation for the indicator function. Other common notations are <math>I_A,</math> and <math>\chi_A.</math>
The indicator function of Шаблон:Mvar is the Iverson bracket of the property of belonging to Шаблон:Mvar; that is,
<math display="block">\mathbf{1}_{A}(x)=[x\in A].</math>
For example, the Dirichlet function is the indicator function of the rational numbers as a subset of the real numbers.
Definition
The indicator function of a subset Шаблон:Mvar of a set Шаблон:Mvar is a function
<math display=block>\mathbf{1}_A \colon X \to \{ 0, 1 \} </math>
defined as
<math display="block" qid="Q371983">\mathbf{1}_A(x) := \begin{cases} 1 ~&\text{ if }~ x \in A~, \\ 0 ~&\text{ if }~ x \notin A~. \end{cases} </math>
The Iverson bracket provides the equivalent notation, <math>[x\in A]</math> or Шаблон:Nowrap to be used instead of <math>\mathbf{1}_{A}(x)\,.</math>
The function <math>\mathbf{1}_A</math> is sometimes denoted Шаблон:Mvar, Шаблон:Mvar, Шаблон:Mvar, or even just Шаблон:Mvar.Шаблон:EfnШаблон:Efn
Notation and terminology
The notation <math>\chi_A</math> is also used to denote the characteristic function in convex analysis, which is defined as if using the reciprocal of the standard definition of the indicator function.
A related concept in statistics is that of a dummy variable. (This must not be confused with "dummy variables" as that term is usually used in mathematics, also called a bound variable.)
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 functionШаблон:Efn to describe the function that indicates membership in a set.
In fuzzy logic and modern many-valued logic, predicates are the characteristic functions of a probability distribution. That is, the strict true/false valuation of the predicate is replaced by a quantity interpreted as the degree of truth.
Basic properties
The indicator or characteristic function of a subset Шаблон:Mvar of some set Шаблон:Mvar maps elements of Шаблон:Mvar to the range <math>\{0,1\}</math>.
This mapping is surjective only when Шаблон:Mvar is a non-empty proper subset of Шаблон:Mvar. If <math>A \equiv X,</math> then <math>\mathbf{1}_A=1.</math> By a similar argument, if <math>A\equiv\emptyset</math> then <math>\mathbf{1}_A=0.</math>
If <math>A</math> and <math>B</math> are two subsets of <math>X,</math> then <math display=block>\begin{align} \mathbf{1}_{A\cap B} &= \min\{\mathbf{1}_A,\mathbf{1}_B\} = \mathbf{1}_A \cdot\mathbf{1}_B, \\ \mathbf{1}_{A\cup B} &= \max\{{\mathbf{1}_A,\mathbf{1}_B}\} = \mathbf{1}_A + \mathbf{1}_B - \mathbf{1}_A \cdot\mathbf{1}_B, \end{align}</math>
and the indicator function of the complement of <math>A</math> i.e. <math>A^C</math> is: <math display=block>\mathbf{1}_{A^\complement} = 1-\mathbf{1}_A.</math>
More generally, suppose <math>A_1, \dotsc, A_n</math> is a collection of subsets of Шаблон:Mvar. For any <math>x \in X:</math>
<math display=block> \prod_{k \in I} ( 1 - \mathbf{1}_{A_k}(x))</math>
is clearly a product of Шаблон:Maths and Шаблон:Maths. This product has the value 1 at precisely those <math>x \in X</math> that belong to none of the sets <math>A_k</math> and is 0 otherwise. That is
<math display=block> \prod_{k \in I} ( 1 - \mathbf{1}_{A_k}) = \mathbf{1}_{X - \bigcup_{k} A_k} = 1 - \mathbf{1}_{\bigcup_{k} A_k}.</math>
Expanding the product on the left hand side,
<math display=block> \mathbf{1}_{\bigcup_{k} A_k}= 1 - \sum_{F \subseteq \{1, 2, \dotsc, n\}} (-1)^{|F|} \mathbf{1}_{\bigcap_F A_k} = \sum_{\emptyset \neq F \subseteq \{1, 2, \dotsc, n\}} (-1)^{|F|+1} \mathbf{1}_{\bigcap_F A_k} </math>
where <math>|F|</math> is the cardinality of Шаблон:Mvar. This is one form of the principle of inclusion-exclusion.
As suggested by the previous example, the indicator function is a useful notational device in combinatorics. The notation is used in other places as well, for instance in probability theory: if Шаблон:Mvar is a probability space with probability measure <math>\operatorname{P}</math> and Шаблон:Mvar is a measurable set, then <math>\mathbf{1}_A</math> becomes a random variable whose expected value is equal to the probability of Шаблон:Mvar:
<math display=block>\operatorname{E}(\mathbf{1}_A)= \int_{X} \mathbf{1}_A(x)\,d\operatorname{P} = \int_{A} d\operatorname{P} = \operatorname{P}(A).</math>
This identity is used in a simple proof of Markov's inequality.
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 <math>\textstyle (\Omega, \mathcal F, \operatorname{P})</math> with <math>A \in \mathcal F,</math> the indicator random variable <math>\mathbf{1}_A \colon \Omega \rightarrow \mathbb{R}</math> is defined by <math>\mathbf{1}_A (\omega) = 1 </math> if <math> \omega \in A,</math> otherwise <math>\mathbf{1}_A (\omega) = 0.</math>
- Mean
- <math>\operatorname{E}(\mathbf{1}_A (\omega)) = \operatorname{P}(A) </math> (also called "Fundamental Bridge").
- Variance
- <math>\operatorname{Var}(\mathbf{1}_A (\omega)) = \operatorname{P}(A)(1 - \operatorname{P}(A)) </math>
- Covariance
- <math> \operatorname{Cov}(\mathbf{1}_A (\omega), \mathbf{1}_B (\omega)) = \operatorname{P}(A \cap B) - \operatorname{P}(A)\operatorname{P}(B) </math>
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 "¬" indicates logical inversion, i.e. "NOT"):[1]Шаблон:Rp
Kleene offers up the same definition in the context of the primitive recursive functions as a function Шаблон:Mvar of a predicate Шаблон:Mvar takes on values Шаблон:Math if the predicate is true and Шаблон:Math if the predicate is false.[2]
For example, because the product of characteristic functions <math>\phi_1 * \phi_2 * \cdots * \phi_n = 0</math> whenever any one of the functions equals Шаблон:Math, it plays the role of logical OR: IF <math>\phi_1 = 0</math> OR <math>\phi_2 = 0</math> OR ... OR <math>\phi_n = 0</math> THEN their product is Шаблон:Math. What appears to the modern reader as the representing function's logical inversion, i.e. the representing function is Шаблон:Math when the function Шаблон:Mvar is "true" or satisfied", plays a useful role in Kleene's definition of the logical functions OR, AND, and IMPLY,[2]Шаблон:Rp the bounded-[2]Шаблон:Rp and unbounded-[2]Шаблон:Rp mu operators and the CASE function.[2]Шаблон:Rp
Characteristic function in fuzzy set theory
In classical mathematics, characteristic functions of sets only take values Шаблон:Math (members) or Шаблон:Math (non-members). In fuzzy set theory, characteristic functions are generalized to take value in the real unit interval Шаблон:Closed-closed, 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.
Smoothness
Шаблон:See also In general, the indicator function of a set is not smooth; it is continuous if and only if its support is a connected component. In the algebraic geometry of finite fields, however, every affine variety admits a (Zariski) continuous indicator function.[3] Given a finite set of functions <math>f_\alpha \in \mathbb{F}_q[x_1,\ldots,x_n]</math> let <math>V = \left\{ x \in \mathbb{F}_q^n : f_\alpha(x) = 0 \right\}</math> be their vanishing locus. Then, the function <math display="inline">P(x) = \prod\left(1 - f_\alpha(x)^{q-1}\right)</math> acts as an indicator function for <math>V</math>. If <math>x \in V</math> then <math>P(x) = 1</math>, otherwise, for some <math>f_\alpha</math>, we have <math>f_\alpha(x) \neq 0</math>, which implies that <math>f_\alpha(x)^{q-1} = 1</math>, hence <math>P(x) = 0</math>.
Although indicator functions are not smooth, they admit weak derivatives. For example, consider Heaviside step function <math display="block">H(x) := \mathbf{1}_{x > 0}</math> The distributional derivative of the Heaviside step function is equal to the Dirac delta function, i.e. <math display=block>\frac{d H(x)}{dx}=\delta(x)</math> and similarly the distributional derivative of <math display="block">G(x) := \mathbf{1}_{x < 0}</math> is <math display=block>\frac{d G(x)}{dx}=-\delta(x)</math>
Thus 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 Шаблон:Mvar. The surface of Шаблон:Mvar will be denoted by Шаблон:Mvar. 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 <math>\delta_S(\mathbf{x})</math>: <math display=block>\delta_S(\mathbf{x}) = -\mathbf{n}_x \cdot \nabla_x\mathbf{1}_{\mathbf{x}\in D}</math> where Шаблон:Mvar is the outward normal of the surface Шаблон:Mvar. This 'surface delta function' has the following property:[4] <math display=block>-\int_{\R^n}f(\mathbf{x})\,\mathbf{n}_x\cdot\nabla_x\mathbf{1}_{\mathbf{x}\in D}\;d^{n}\mathbf{x} = \oint_{S}\,f(\mathbf{\beta})\;d^{n-1}\mathbf{\beta}.</math>
By setting the function Шаблон:Mvar equal to one, it follows that the inward normal derivative of the indicator integrates to the numerical value of the surface area Шаблон:Mvar.
See also
- Dirac measure
- Laplacian of the indicator
- Dirac delta
- Extension (predicate logic)
- Free variables and bound variables
- Heaviside step function
- Identity function
- Iverson bracket
- Kronecker delta, a function that can be viewed as an indicator for the identity relation
- Macaulay brackets
- Multiset
- Membership function
- Simple function
- Dummy variable (statistics)
- Statistical classification
- Zero-one loss function
Notes
References
Sources
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