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chi_squared_distribution Class

Generates a chi-squared distribution.

template<class RealType = double>
class chi_squared_distribution
{
public:
    // types
    typedef RealType result_type;
    struct param_type;
    // constructor and reset functions
    explicit chi_squared_distribution(RealType n = 1);
    explicit chi_squared_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 n() const;
    param_type param() const;
    void param(const param_type& parm);
    result_type min() const;
    result_type max() const;
};

RealType

The floating-point result type, defaults to double. For possible types, see <random>.

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 Chi-Squared Distribution. The following table links to articles about individual members.

chi_squared_distribution Class

chi_squared_distribution::n

chi_squared_distribution::param

chi_squared_distribution::operator()

chi_squared_distribution::param_type

The property function n() returns the value for the stored distribution parameter n.

For more information about distribution classes and their members, see <random>.

For detailed information about the chi-squared distribution, see the Wolfram MathWorld article Chi-Squared Distribution.

 

// compile with: /EHsc /W4
#include <random> 
#include <iostream>
#include <iomanip>
#include <string>
#include <map>

void test(const double n, const int s) {

    // uncomment to use a non-deterministic generator
    //    std::random_device gen;
    std::mt19937 gen(1701);

    std::chi_squared_distribution<> distr(n);

    std::cout << std::endl;
    std::cout << "min() == " << distr.min() << std::endl;
    std::cout << "max() == " << distr.max() << 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 n_dist = 0.5;
    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 \'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(n_dist, samples);
}

First run:

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'n' distribution parameter (must be greater than zero): .5
Enter an integer value for the sample count: 10

min() == 4.94066e-324
max() == 1.79769e+308
n() == 0.5000000000
Distribution for 10 samples:
          1:   0.0007625595
          2:   0.0016895062
          3:   0.0058683478
          4:   0.0189647765
          5:   0.0556619371
          6:   0.1448191353
          7:   0.1448245325
          8:   0.1903494379
          9:   0.9267525768
         10:   1.5429743723

Second run:

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'n' distribution parameter (must be greater than zero): .3333
Enter an integer value for the sample count: 10

min() == 4.94066e-324
max() == 1.79769e+308
n() == 0.3333000000
Distribution for 10 samples:
          1:   0.0000148725
          2:   0.0000490528
          3:   0.0003175988
          4:   0.0018454535
          5:   0.0092808795
          6:   0.0389540735
          7:   0.0389562514
          8:   0.0587028468
          9:   0.6183666639
         10:   1.3552086624

Third run:

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'n' distribution parameter (must be greater than zero): 1000
Enter an integer value for the sample count: 10

min() == 4.94066e-324
max() == 1.79769e+308
n() == 1000.0000000000
Distribution for 10 samples:
          1: 958.5284624473
          2: 958.7882787809
          3: 963.0667684792
          4: 987.9638091514
          5: 1016.2433493745
          6: 1021.9337111110
          7: 1021.9723046240
          8: 1035.7622110505
          9: 1043.8725156645
         10: 1054.7051509381

Header: <random>

Namespace: std

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