gamma_distribution Class
Generates a gamma distribution.
template<class RealType = double>
class gamma_distribution
{
public:
// types
typedef RealType result_type;
struct param_type;
// constructors and reset functions
explicit gamma_distribution(RealType alpha = 1.0, RealType beta = 1.0);
explicit gamma_distribution(const param_type& parm);
void reset();
// generating functions
template<class URNG>
result_type operator()(URNG& gen);
template<class URNG>
result_type operator()(URNG& gen, const param_type& parm);
// property functions
RealType alpha() const;
RealType beta() const;
param_type param() const;
void param(const param_type& parm);
result_type min() const;
result_type max() const;
};
Parameters
- RealType
The floating-point result type, defaults to double. For possible types, see <random>.
Remarks
The template class describes a distribution that produces values of a user-specified integral type, or type double if none is provided, distributed according to the Gamma Distribution. The following table links to articles about individual members.
gamma_distribution::alpha |
gamma_distribution::param |
|
gamma_distribution::operator() |
gamma_distribution::beta |
The property functions alpha() and beta() return their respective values for stored distribution parameters alpha and beta.
For more information about distribution classes and their members, see <random>.
For detailed information about the gamma distribution, see the Wolfram MathWorld article Gamma Distribution.
Example
// compile with: /EHsc /W4
#include <random>
#include <iostream>
#include <iomanip>
#include <string>
#include <map>
void test(const double a, const double b, const int s) {
// uncomment to use a non-deterministic generator
// std::random_device gen;
std::mt19937 gen(1701);
std::gamma_distribution<> distr(a, b);
std::cout << std::endl;
std::cout << "min() == " << distr.min() << std::endl;
std::cout << "max() == " << distr.max() << std::endl;
std::cout << "alpha() == " << std::fixed << std::setw(11) << std::setprecision(10) << distr.alpha() << std::endl;
std::cout << "beta() == " << std::fixed << std::setw(11) << std::setprecision(10) << distr.beta() << std::endl;
// generate the distribution as a histogram
std::map<double, int> histogram;
for (int i = 0; i < s; ++i) {
++histogram[distr(gen)];
}
// print results
std::cout << "Distribution for " << s << " samples:" << std::endl;
int counter = 0;
for (const auto& elem : histogram) {
std::cout << std::fixed << std::setw(11) << ++counter << ": "
<< std::setw(14) << std::setprecision(10) << elem.first << std::endl;
}
std::cout << std::endl;
}
int main()
{
double a_dist = 0.0;
double b_dist = 1;
int samples = 10;
std::cout << "Use CTRL-Z to bypass data entry and run using default values." << std::endl;
std::cout << "Enter a floating point value for the 'alpha' distribution parameter (must be greater than zero): ";
std::cin >> a_dist;
std::cout << "Enter a floating point value for the 'beta' distribution parameter (must be greater than zero): ";
std::cin >> b_dist;
std::cout << "Enter an integer value for the sample count: ";
std::cin >> samples;
test(a_dist, b_dist, samples);
}
Output
Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'alpha' distribution parameter (must be greater than zero): 1
Enter a floating point value for the 'beta' distribution parameter (must be greater than zero): 1
Enter an integer value for the sample count: 10
min() == 4.94066e-324
max() == 1.79769e+308
alpha() == 1.0000000000
beta() == 1.0000000000
Distribution for 10 samples:
1: 0.0936880533
2: 0.1225944894
3: 0.6443593183
4: 0.6551171649
5: 0.7313457551
6: 0.7313557977
7: 0.7590097389
8: 1.4466885214
9: 1.6434088411
10: 2.1201210996
Requirements
Header: <random>
Namespace: std