binomial_distribution Class

Generates a binomial distribution.

template<class IntType = int>
class binomial_distribution
{
public:
    // types
    typedef IntType result_type;
    struct param_type;
    // constructors and reset functions
    explicit binomial_distribution(IntType t = 1, double p = 0.5);
    explicit binomial_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
    IntType t() const;
    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, or type int if none is provided, distributed according to the Binomial Distribution discrete probability function. The following table links to articles about individual members.

The property members t() and p() return the currently stored distribution parameter values t and p respectively.

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

For detailed information about the binomial distribution discrete probability function, see the Wolfram MathWorld article Binomial Distribution.

Example

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

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

    // uncomment to use a non-deterministic seed
    //    std::random_device rd;
    //    std::mt19937 gen(rd());
    std::mt19937 gen(1729);

    std::binomial_distribution<> distr(t, p);

    std::cout << std::endl;
    std::cout << "p == " << distr.p() << std::endl;
    std::cout << "t == " << distr.t() << 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 << "Histogram 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()
{
    int    t_dist = 1;
    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 an integer value for t distribution (where 0 <= t): ";
    std::cin >> t_dist;
    std::cout << "Enter a double value for p distribution (where 0.0 <= p <= 1.0): ";
    std::cin >> p_dist;
    std::cout << "Enter an integer value for a sample count: ";
    std::cin >> samples;

    test(t_dist, p_dist, samples);
}

Output

First run:

Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for t distribution (where 0 <= t): 22
Enter a double value for p distribution (where 0.0 <= p <= 1.0): .25
Enter an integer value for a sample count: 100

p == 0.25
t == 22
Histogram for 100 samples:
    1 :
    2 ::
    3 :::::::::::::
    4 ::::::::::::::
    5 :::::::::::::::::::::::::
    6 ::::::::::::::::::
    7 :::::::::::::
    8 ::::::
    9 ::::::
   11 :
   12 :

Second run:

Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for t distribution (where 0 <= t): 22
Enter a double value for p distribution (where 0.0 <= p <= 1.0): .5
Enter an integer value for a sample count: 100

p == 0.5
t == 22
Histogram for 100 samples:
    6 :
    7 ::
    8 :::::::::
    9 ::::::::::
   10 ::::::::::::::::
   11 :::::::::::::::::::
   12 :::::::::::
   13 :::::::::::::
   14 :::::::::::::::
   15 ::
   16 ::

Third run:

Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for t distribution (where 0 <= t): 22
Enter a double value for p distribution (where 0.0 <= p <= 1.0): .75
Enter an integer value for a sample count: 100

p == 0.75
t == 22
Histogram for 100 samples:
   13 ::::
   14 :::::::::::
   15 :::::::::::::::
   16 :::::::::::::::::::::
   17 ::::::::::::::
   18 :::::::::::::::::
   19 :::::::::::
   20 ::::::
   21 :

Requirements

Header: <random>

Namespace: std

See Also

Reference

<random>