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.

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

See Also

Reference

<random>