uniform_int_distribution Class
The latest version of this topic can be found at uniform_int_distribution Class.
Generates a uniform (every value is equally probable) integer distribution within an output range that is inclusive-inclusive.
Syntax
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;};
Parameters
IntType
The integer result type, defaults to int. For possible types, see <random>.
Remarks
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>.
Example
// 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);
}
Output
Use CTRL-Z to bypass data entry and run using default values.Enter an integer value for the lower bound of the distribution: 0Enter an integer value for the upper bound of the distribution: 12Enter an integer value for the sample count: 200lower bound == 0upper bound == 12Distribution for 200 samples: 0 ::::::::::::::: 1 ::::::::::::::::::::: 2 :::::::::::::::::: 3 ::::::::::::::: 4 ::::::: 5 ::::::::::::::::::::: 6 ::::::::::::: 7 :::::::::: 8 ::::::::::::::: 9 ::::::::::::: 10 :::::::::::::::::::::: 11 ::::::::::::: 12 :::::::::::::::::
Requirements
Header: <random>
Namespace: std
uniform_int_distribution::uniform_int_distribution
Constructs the distribution.
explicit uniform_int_distribution(result_type a = 0, result_type b = std::numeric_limits<IntType>::max());
explicit uniform_int_distribution(const param_type& parm);
Parameters
a
The lower bound for random values, inclusive.
b
The upper bound for random values, inclusive.
parm
The parameter structure used to construct the distribution.
Remarks
Precondition: a ≤ b
The first constructor constructs an object whose stored a value holds the value a and whose stored b value holds the value b.
The second constructor constructs an object whose stored parameters are initialized from parm. You can obtain and set the current parameters of an existing distribution by calling the param() member function.
uniform_int_distribution::param_type
Stores the parameters of the distribution.
struct param_type {
typedef uniform_int_distribution<IntType> distribution_type;
param_type(IntType a = 0, IntType b = std::numeric_limits<IntType>::max());
result_type a() const;
result_type b() const;
.....
bool operator==(const param_type& right) const;
bool operator!=(const param_type& right) const;
};
Parameters
See parent topic uniform_int_distribution Class.
Remarks
Precondition: a ≤ b
This structure can be passed to the distribution's class constructor at instantiation, to the param() member function to set the stored parameters of an existing distribution, and to operator() to be used in place of the stored parameters.