discrete_distribution Class

Generates a discrete integer distribution that has uniform-width intervals with uniform probability in each interval.

template<class IntType = int>
class discrete_distribution
{
public:
    // types
    typedef IntType result_type;
    struct param_type;
    // constructor and reset functions
    discrete_distribution();
    template<class InputIterator>
    discrete_distribution(InputIterator firstW, InputIterator lastW);
    discrete_distribution(initializer_list<double> weightlist);
    template<class UnaryOperation>
    discrete_distribution(size_t count, double xmin, double xmax, UnaryOperation funcweight);
    explicit discrete_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
    vector<double> probabilities() 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

This sampling distribution has uniform-width intervals with uniform probability in each interval. For information about other sampling distributions, see piecewise_linear_distribution Class and piecewise_constant_distribution Class.

The following table links to articles about individual members:

discrete_distribution::discrete_distribution	discrete_distribution::param
discrete_distribution::operator()	discrete_distribution::param_type

The property function vector<double> probabilities() returns the individual probabilities for each integer generated.

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

Example

 

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

using namespace std;

void test(const int s) {

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

    discrete_distribution<> distr({ 1, 2, 3, 4, 5 });

    cout << endl;
    cout << "min() == " << distr.min() << endl;
    cout << "max() == " << distr.max() << endl;
    cout << "probabilities (value: probability):" << endl;
    vector<double> p = distr.probabilities();
    int counter = 0;
    for (const auto& n : p) {
        cout << fixed << setw(11) << counter << ": " << setw(14) << setprecision(10) << n << endl;
        ++counter;
    }
    cout << endl;

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

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

int main()
{
    int samples = 100;

    cout << "Use CTRL-Z to bypass data entry and run using default values." << endl;
    cout << "Enter an integer value for the sample count: ";
    cin >> samples;

    test(samples);
}

Output

Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for the sample count: 100

min() == 0
max() == 4
probabilities (value: probability):
          0:   0.0666666667
          1:   0.1333333333
          2:   0.2000000000
          3:   0.2666666667
          4:   0.3333333333

Distribution for 100 samples:
    0 :::::
    1 ::::::::::::::
    2 :::::::::::::::::
    3 ::::::::::::::::::::::::::::::
    4 ::::::::::::::::::::::::::::::::::

Requirements

Header: <random>

Namespace: std

See Also

Reference

<random>