double erf ( double num );
/*floating-point-type*/
erf ( /*floating-point-type*/ num );
float erff( float num );
(2) (since C++11)long double erfl( long double num );
(3) (since C++11)constexpr /*deduced-simd-t*/<V>
template< class Integer >
double erf ( Integer num );
Computes the
error functionof
num.
The library provides overloads ofstd::erf
for all cv-unqualified floating-point types as the type of the parameter.(since C++23)
A) Additional overloads are provided for all integer types, which are treated as double.
(since C++11) [edit] Parameters num - floating-point or integer value [edit] Return valueIf no errors occur, value of the error function of
num, that is
\(\frac{2}{\sqrt{\pi} }\int_{0}^{num}{e^{-{t^2} }\mathsf{d}t}\)∫num, is returned.
If a range error occurs due to underflow, the correct result (after rounding), that is
\(\frac{2\cdot num}{\sqrt{\pi} }\)is returned.
[edit] Error handlingErrors are reported as specified in math_errhandling.
If the implementation supports IEEE floating-point arithmetic (IEC 60559),
Underflow is guaranteed if |num| < DBL_MIN * (std::sqrt(Ï) / 2).
\(\operatorname{erf}(\frac{x}{\sigma \sqrt{2} })\)erf()is the probability that a measurement whose errors are subject to a normal distribution with standard deviation
\(\sigma\)σis less than
\(x\)xaway from the mean value.
The additional overloads are not required to be provided exactly as (A). They only need to be sufficient to ensure that for their argument num of integer type, std::erf(num) has the same effect as std::erf(static_cast<double>(num)).
[edit] ExampleThe following example calculates the probability that a normal variate is on the interval (x1, x2):
#include <cmath> #include <iomanip> #include <iostream> double phi(double x1, double x2) { return (std::erf(x2 / std::sqrt(2)) - std::erf(x1 / std::sqrt(2))) / 2; } int main() { std::cout << "Normal variate probabilities:\n" << std::fixed << std::setprecision(2); for (int n = -4; n < 4; ++n) std::cout << '[' << std::setw(2) << n << ':' << std::setw(2) << n + 1 << "]: " << std::setw(5) << 100 * phi(n, n + 1) << "%\n"; std::cout << "Special values:\n" << "erf(-0) = " << std::erf(-0.0) << '\n' << "erf(Inf) = " << std::erf(INFINITY) << '\n'; }
Output:
Normal variate probabilities: [-4:-3]: 0.13% [-3:-2]: 2.14% [-2:-1]: 13.59% [-1: 0]: 34.13% [ 0: 1]: 34.13% [ 1: 2]: 13.59% [ 2: 3]: 2.14% [ 3: 4]: 0.13% Special values: erf(-0) = -0.00 erf(Inf) = 1.00[edit] See also [edit] External links
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