uniform_real_distribution Class

The latest version of this topic can be found at uniform_real_distribution Class.

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

Syntax

class uniform_real_distribution {
public:
    // types 
    typedef RealType result_type;
    struct param_type;
    // constructors and reset functions 
    explicit uniform_real_distribution(RealType a = 0.0, RealType b = 1.0);
    explicit uniform_real_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

RealType
The floating-point result type, defaults to double. For possible types, see <random>.

Remarks

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

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 double a, const double 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_real_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<double, int> histogram;  
    for (int i = 0; i < s; ++i) {  
        ++histogram[distr(gen)];  
    }  
  
    // print results  
    std::cout << "Distribution for " << s << " samples:" << std::endl;  
    int counter = 0;  
    for (const auto& elem : histogram) {  
        std::cout << std::fixed << std::setw(11) << ++counter << ": "   
            << std::setprecision(10) << elem.first << std::endl;  
    }  
    std::cout << std::endl;  
}  
  
int main()  
{  
    double a_dist = 1.0;  
    double b_dist = 1.5;  
  
    int samples = 10;  
  
    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 lower bound of the distribution: ";  
    std::cin >> a_dist;  
    std::cout << "Enter a floating point 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 a floating point value for the lower bound of the distribution: .5Enter a floating point value for the upper bound of the distribution: 1Enter an integer value for the sample count: 20lower bound == 0.5upper bound == 1Distribution for 20 samples:          1: 0.5144304741          2: 0.6003997192          3: 0.6060792968          4: 0.6270416650          5: 0.6295091197          6: 0.6437749373          7: 0.6513740058          8: 0.7062379346          9: 0.7117609406         10: 0.7206888566         11: 0.7423223702         12: 0.7826033033         13: 0.8112872958         14: 0.8440467608         15: 0.8461254641         16: 0.8598305065         17: 0.8640874069         18: 0.8770968361         19: 0.9397858282         20: 0.9804645012  

Requirements

Header: <random>

Namespace: std

uniform_real_distribution::uniform_real_distribution

Constructs the distribution.

explicit uniform_real_distribution(RealType a = 0.0, RealType b = 1.0);

 
explicit uniform_real_distribution(const param_type& parm);

Parameters

a
The lower bound for random values, inclusive.

b
The upper bound for random values, exclusive.

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_real_distribution::param_type

Stores all the parameters of the distribution.

struct param_type {  
   typedef uniform_real_distribution<RealType> distribution_type;  
   param_type(RealType a = 0.0, RealType b = 1.0);
   RealType a() const;
   RealType b() const;
   .....  
   bool operator==(const param_type& right) const;
   bool operator!=(const param_type& right) const;
   };  

Parameters

See parent topic uniform_real_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.

See Also

<random>