geometric_distribution Class

Generates a geometric distribution.

template<class IntType = int>
class geometric_distribution
{
public:
    // types
    typedef IntType result_type;
    struct param_type;
    // constructors and reset functions
    explicit geometric_distribution(double p = 0.5);
    explicit geometric_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
    double p() const;
    param_type param() const;
    void param(const param_type& parm);
    result_type min() const;
    result_type max() const;
};

Parameters

• IntType
  The integer result type, defaults to int. For possible types, see <random>.

Remarks

The template class describes a distribution that produces values of a user-specified integral type with a geometric distribution. The following table links to articles about individual members.

geometric_distribution::geometric_distribution	geometric_distribution::p	geometric_distribution::param
geometric_distribution::operator()		geometric_distribution::param_type

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

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

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

Example

 

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

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

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

    std::geometric_distribution<> distr(p);

    std::cout << std::endl;
    std::cout << "min() == " << distr.min() << std::endl;
    std::cout << "max() == " << distr.max() << std::endl;
    std::cout << "p() == " << std::fixed << std::setw(11) << std::setprecision(10) << distr.p() << std::endl;

    // generate the distribution as a histogram
    std::map<int, int> histogram;
    for (int i = 0; i < s; ++i) {
        ++histogram[distr(gen)];
    }

    // print results
    std::cout << "Distribution for " << s << " samples:" << std::endl;
    for (const auto& elem : histogram) {
        std::cout << std::setw(5) << elem.first << ' ' << std::string(elem.second, ':') << std::endl;
    }
    std::cout << std::endl;
}

int main()
{
    double p_dist = 0.5;

    int samples = 100;

    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 \'p\' distribution parameter: ";
    std::cin >> p_dist;
    std::cout << "Enter an integer value for the sample count: ";
    std::cin >> samples;

    test(p_dist, samples);
}

Output

First test:

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'p' distribution parameter: .5
Enter an integer value for the sample count: 100

min() == 0
max() == 2147483647
p() == 0.5000000000
Distribution for 100 samples:
    0 ::::::::::::::::::::::::::::::::::::::::::::::::::::
    1 ::::::::::::::::::::::::
    2 ::::::::::::::
    3 :::::
    4 ::
    5 ::
    6 :

Second test:

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'p' distribution parameter: .1
Enter an integer value for the sample count: 100

min() == 0
max() == 2147483647
p() == 0.1000000000
Distribution for 100 samples:
    0 :::::::::
    1 :::::::::::
    2 :::::::
    3 ::::::::
    4 ::::::::
    5 ::::::
    6 :::::
    7 ::::::
    8 :::::
    9 ::::
   10 ::::
   11 ::
   12 :
   13 :
   14 :::
   15 ::::
   16 :::
   17 :
   18 :
   19 :
   20 ::
   21 :
   22 :
   23 :
   28 ::
   33 :
   35 :
   40 :

Requirements

Header: <random>

Namespace: std

See Also

Reference

<random>