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;
};

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

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.

Example

// 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);
}

Output

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

Requirements

Header: <random>

Namespace: std

See Also

Reference

<random>