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.

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>