fisher_f_distribution 类

生成 Fisher F 分布。

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; };

参数

• RealType
  浮点结果类型，默认为 double。 有关可能的类型，请参阅 <random>。

备注

如果未根据 Fisher 的 F-分布提供和分布任何类型，则模板类将描述产生用户指定的整型值或 double 型值的分布。 下表链接到有关各个成员的文章。

fisher_f_distribution::fisher_f_distribution	fisher_f_distribution::m	fisher_f_distribution::param
fisher_f_distribution::operator()	fisher_f_distribution::n	fisher_f_distribution::param_type

属性函数 m() 和 n() 将分别返回存储的分布参数 m 和 n 的值。

有关分布类及其成员的详细信息，请参阅 <random>。

有关 Fisher 的 F-分布的详细信息，请参阅 Wolfram MathWorld 文章 F-分布。

示例

 

// 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);
}

输出

首次运行：

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

第二次运行：

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

第三次运行：

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

要求

标头：<random>

命名空间: std

请参见

参考

<random>