poisson_distribution Class

Generates a Poisson distribution.

template<class IntType = int>
class poisson_distribution
{
public:
    // types
    typedef IntType result_type;
    struct param_type;
    // constructors and reset functions
    explicit poisson_distribution(double mean = 1.0);
    explicit poisson_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 mean() 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 Poisson distribution. The following table links to articles about individual members.

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

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

For detailed information about the Poisson distribution, see the Wolfram MathWorld article Poisson 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::poisson_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.mean() << 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 = 1.0;

    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 'mean' distribution parameter (must be greater than zero): ";
    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 'mean' distribution parameter (must be greater than zero): 1
Enter an integer value for the sample count: 100

min() == 0
max() == 2147483647
p() == 1.0000000000
Distribution for 100 samples:
    0 ::::::::::::::::::::::::::::::
    1 ::::::::::::::::::::::::::::::::::::::
    2 :::::::::::::::::::::::
    3 ::::::::
    5 :

Second test:

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'mean' distribution parameter (must be greater than zero): 10
Enter an integer value for the sample count: 100

min() == 0
max() == 2147483647
p() == 10.0000000000
Distribution for 100 samples:
    3 :
    4 ::
    5 ::
    6 ::::::::
    7 ::::
    8 ::::::::
    9 ::::::::::::::
   10 ::::::::::::
   11 ::::::::::::::::
   12 :::::::::::::::
   13 ::::::::
   14 ::::::
   15 :
   16 ::
   17 :

Requirements

Header: <random>

Namespace: std

See Also

Reference

<random>