...one of the most highly
regarded and expertly designed C++ library projects in the
world.
— Herb Sutter and Andrei
Alexandrescu, C++
Coding Standards
boost::random::chi_squared_distribution
// In header: <boost/random/chi_squared_distribution.hpp> template<typename RealType = double> class chi_squared_distribution { public: // types typedef RealType result_type; typedef RealType input_type; // member classes/structs/unions class param_type { public: // types typedef chi_squared_distribution distribution_type; // public member functions explicit param_type(RealType = 1); RealType n() const; // friend functions template<typename CharT, typename Traits> std::basic_ostream< CharT, Traits > & operator<<(std::basic_ostream< CharT, Traits > &, const param_type &); template<typename CharT, typename Traits> std::basic_istream< CharT, Traits > & operator>>(std::basic_istream< CharT, Traits > &, param_type &); bool operator==(const param_type &, const param_type &); bool operator!=(const param_type &, const param_type &); }; // public member functions explicit chi_squared_distribution(RealType = 1); explicit chi_squared_distribution(const param_type &); template<typename URNG> RealType operator()(URNG &); template<typename URNG> RealType operator()(URNG &, const param_type &) const; RealType n() const; RealType min() const; RealType max() const; param_type param() const; void param(const param_type &); void reset(); // friend functions template<typename CharT, typename Traits> std::basic_ostream< CharT, Traits > & operator<<(std::basic_ostream< CharT, Traits > &, const chi_squared_distribution &); template<typename CharT, typename Traits> std::basic_istream< CharT, Traits > & operator>>(std::basic_istream< CharT, Traits > &, chi_squared_distribution &); bool operator==(const chi_squared_distribution &, const chi_squared_distribution &); bool operator!=(const chi_squared_distribution &, const chi_squared_distribution &); };
The chi squared distribution is a real valued distribution with one parameter, n
. The distribution produces values > 0.
The distribution function is .
chi_squared_distribution
public member functionsexplicit chi_squared_distribution(RealType n = 1);
Construct a chi_squared_distribution
object. n
is the parameter of the distribution.
Requires: t >=0 && 0 <= p <= 1
explicit chi_squared_distribution(const param_type & param);
Construct an chi_squared_distribution
object from the parameters.
template<typename URNG> RealType operator()(URNG & urng);
Returns a random variate distributed according to the chi squared distribution.
template<typename URNG> RealType operator()(URNG & urng, const param_type & param) const;
Returns a random variate distributed according to the chi squared distribution with parameters specified by param
.
RealType n() const;
Returns the n
parameter of the distribution.
RealType min() const;
Returns the smallest value that the distribution can produce.
RealType max() const;
Returns the largest value that the distribution can produce.
param_type param() const;
Returns the parameters of the distribution.
void param(const param_type & param);
Sets parameters of the distribution.
void reset();
Effects: Subsequent uses of the distribution do not depend on values produced by any engine prior to invoking reset.
chi_squared_distribution
friend functionstemplate<typename CharT, typename Traits> std::basic_ostream< CharT, Traits > & operator<<(std::basic_ostream< CharT, Traits > & os, const chi_squared_distribution & c2d);
Writes the parameters of the distribution to a std::ostream
.
template<typename CharT, typename Traits> std::basic_istream< CharT, Traits > & operator>>(std::basic_istream< CharT, Traits > & is, chi_squared_distribution & c2d);
Reads the parameters of the distribution from a std::istream
.
bool operator==(const chi_squared_distribution & lhs, const chi_squared_distribution & rhs);
Returns true if the two distributions will produce the same sequence of values, given equal generators.
bool operator!=(const chi_squared_distribution & lhs, const chi_squared_distribution & rhs);
Returns true if the two distributions could produce different sequences of values, given equal generators.