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uniform_int_distribution Class

Generates a uniform (every value is equally probable) integer distribution within an output range that is inclusive-inclusive.

template<class IntType = int>
class uniform_int_distribution
{
public:
    // types
    typedef IntType result_type;
    struct param_type;
    // constructors and reset functions
    explicit uniform_int_distribution(IntType a = 0, IntType b = numeric_limits<IntType>::max());
    explicit uniform_int_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
    result_type a() const;
    result_type b() const;
    param_type param() const;
    void param(const param_type& parm);
    result_type min() const;
    result_type max() const;
};

IntType

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

The template class describes an inclusive-inclusive distribution that produces values of a user-specified integral type with a distribution so that every value is equally probable. The following table links to articles about individual members.

uniform_int_distribution::uniform_int_distribution

uniform_int_distribution::a

uniform_int_distribution::param

uniform_int_distribution::operator()

uniform_int_distribution::b

uniform_int_distribution::param_type

The property member a() returns the currently stored minimum bound of the distribution, while b() returns the currently stored maximum bound. For this distribution class, these minimum and maximum values are the same as those returned by the common property functions min() and max() described in the <random> topic.

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

 

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

void test(const int a, const int b, const int s) {

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

    std::uniform_int_distribution<> distr(a, b);

    std::cout << "lower bound == " << distr.a() << std::endl;
    std::cout << "upper bound == " << distr.b() << 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()
{
    int a_dist = 1;
    int b_dist = 10;

    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 the lower bound of the distribution: ";
    std::cin >> a_dist;
    std::cout << "Enter an integer value for the upper bound of the distribution: ";
    std::cin >> b_dist;
    std::cout << "Enter an integer value for the sample count: ";
    std::cin >> samples;

    test(a_dist, b_dist, samples);
}

Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for the lower bound of the distribution: 0
Enter an integer value for the upper bound of the distribution: 12
Enter an integer value for the sample count: 200
lower bound == 0
upper bound == 12
Distribution for 200 samples:
    0 :::::::::::::::
    1 :::::::::::::::::::::
    2 ::::::::::::::::::
    3 :::::::::::::::
    4 :::::::
    5 :::::::::::::::::::::
    6 :::::::::::::
    7 ::::::::::
    8 :::::::::::::::
    9 :::::::::::::
   10 ::::::::::::::::::::::
   11 :::::::::::::
   12 :::::::::::::::::

Header: <random>

Namespace: std

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