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chi_squared_distribution Class

 

The latest version of this topic can be found at chi_squared_distribution Class.

Generates a chi-squared distribution.

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>.

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::chi_squared_distributionchi_squared_distribution::nchi_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

Constructs the distribution.

explicit chi_squared_distribution(RealType n = 1.0);

 
explicit chi_squared_distribution(const param_type& parm);

Parameters

n
The n distribution parameter.

parm
The parameter structure used to construct the distribution.

Remarks

Precondition: 0.0 < n

The first constructor constructs an object whose stored n value holds the value n.

The second constructor constructs an object whose stored parameters are initialized from parm. You can obtain and set the current parameters of an existing distribution by calling the param() member function.

Stores the parameters of the distribution.

struct param_type {  
   typedef chi_squared_distribution<RealType> distribution_type;  
   param_type(RealType n = 1.0);
   RealType n() const;
   .....  
   bool operator==(const param_type& right) const;
   bool operator!=(const param_type& right) const;
   };  

Parameters

See parent topic chi_squared_distribution Class.

Remarks

Precondition: 0.0 < n

This structure can be passed to the distribution's class constructor at instantiation, to the param() member function to set the stored parameters of an existing distribution, and to operator() to be used in place of the stored parameters.

<random>

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