fisher_f_distribution Class
Generates a Fisher F distribution.
template<class RealType = double>
class fisher_f_distribution
{
public:
// types
typedef RealType result_type;
struct param_type;
// constructor and reset functions
explicit fisher_f_distribution(RealType m = 1.0, RealType n = 1.0);
explicit fisher_f_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 m() const;
RealType n() 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 Fisher's F-Distribution. The following table links to articles about individual members.
fisher_f_distribution::m |
fisher_f_distribution::param |
|
fisher_f_distribution::operator() |
fisher_f_distribution::n |
The property functions m() and n() return the values for the stored distribution parameters m and n respectively.
For more information about distribution classes and their members, see <random>.
For detailed information about the F- distribution, see the Wolfram MathWorld article F-Distribution.
Example
// compile with: /EHsc /W4
#include <random>
#include <iostream>
#include <iomanip>
#include <string>
#include <map>
void test(const double m, const double n, const int s) {
// uncomment to use a non-deterministic seed
// std::random_device rd;
// std::mt19937 gen(rd());
std::mt19937 gen(1701);
std::fisher_f_distribution<> distr(m, n);
std::cout << std::endl;
std::cout << "min() == " << distr.min() << std::endl;
std::cout << "max() == " << distr.max() << std::endl;
std::cout << "m() == " << std::fixed << std::setw(11) << std::setprecision(10) << distr.m() << std::endl;
std::cout << "n() == " << std::fixed << std::setw(11) << std::setprecision(10) << distr.n() << 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 m_dist = 1;
double n_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 \'m\' distribution parameter (must be greater than zero): ";
std::cin >> m_dist;
std::cout << "Enter a floating point value for the \'n\' distribution parameter (must be greater than zero): ";
std::cin >> n_dist;
std::cout << "Enter an integer value for the sample count: ";
std::cin >> samples;
test(m_dist, n_dist, samples);
}
Output
First run:
Enter a floating point value for the 'm' distribution parameter (must be greater than zero): 1
Enter a floating point value for the 'n' distribution parameter (must be greater than zero): 1
Enter an integer value for the sample count: 10
min() == 0
max() == 1.79769e+308
m() == 1.0000000000
n() == 1.0000000000
Distribution for 10 samples:
1: 0.0204569549
2: 0.0221376644
3: 0.0297234962
4: 0.1600937252
5: 0.2775342196
6: 0.3950701700
7: 0.8363200295
8: 0.9512500702
9: 2.7844815974
10: 3.4320929653
Second run:
Enter a floating point value for the 'm' distribution parameter (must be greater than zero): 1
Enter a floating point value for the 'n' distribution parameter (must be greater than zero): .1
Enter an integer value for the sample count: 10
min() == 0
max() == 1.79769e+308
m() == 1.0000000000
n() == 0.1000000000
Distribution for 10 samples:
1: 0.0977725649
2: 0.5304122767
3: 4.9468518084
4: 25.1012074939
5: 48.8082121613
6: 401.8075539377
7: 8199.5947873699
8: 226492.6855335717
9: 2782062.6639740225
10: 20829747131.7185860000
Third run:
Enter a floating point value for the 'm' distribution parameter (must be greater than zero): .1
Enter a floating point value for the 'n' distribution parameter (must be greater than zero): 1
Enter an integer value for the sample count: 10
min() == 0
max() == 1.79769e+308
m() == 0.1000000000
n() == 1.0000000000
Distribution for 10 samples:
1: 0.0000000000
2: 0.0000000000
3: 0.0000000000
4: 0.0000000000
5: 0.0000000033
6: 0.0000073975
7: 0.0000703800
8: 0.0280427735
9: 0.2660239949
10: 3.4363333954
Requirements
Header: <random>
Namespace: std