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exponential_distribution Class

Generates an exponential distribution.

template<class RealType = double>
class exponential_distribution
{
public:
    // types
    typedef RealType result_type;
    struct param_type;
    // constructors and reset functions
    explicit exponential_distribution(RealType lambda = 1.0);
    explicit exponential_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
    RealType lambda() const;
    param_type param() const;
    void param(const param_type& parm);
    result_type min() const;
    result_type max() const;
};

RealType

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

The template class describes a distribution that produces values of a user-specified integral type, or type double if none is provided, distributed according to the Exponential Distribution. The following table links to articles about individual members.

exponential_distribution::exponential_distribution

exponential_distribution::lambda

exponential_distribution::param

exponential_distribution::operator()

exponential_distribution::param_type

The property function lambda() returns the value for the stored distribution parameter lambda.

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

For detailed information about the exponential distribution, see the Wolfram MathWorld article Exponential Distribution.

 

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

void test(const double l, const int s) {

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

    std::exponential_distribution<> distr(l);

    std::cout << std::endl;
    std::cout << "min() == " << distr.min() << std::endl;
    std::cout << "max() == " << distr.max() << std::endl;
    std::cout << "lambda() == " << std::fixed << std::setw(11) << std::setprecision(10) << distr.lambda() << 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::setw(14) << std::setprecision(10) << elem.first << std::endl;
    }
    std::cout << std::endl;
}

int main()
{
    double l_dist = 0.5;
    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 'lambda' distribution parameter (must be greater than zero): ";
    std::cin >> l_dist;
    std::cout << "Enter an integer value for the sample count: ";
    std::cin >> samples;

    test(l_dist, samples);
}

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'lambda' distribution parameter (must be greater than zero): 1
Enter an integer value for the sample count: 10

min() == 0
max() == 1.79769e+308
lambda() == 1.0000000000
Distribution for 10 samples:
          1:   0.0936880533
          2:   0.1225944894
          3:   0.6443593183
          4:   0.6551171649
          5:   0.7313457551
          6:   0.7313557977
          7:   0.7590097389
          8:   1.4466885214
          9:   1.6434088411
         10:   2.1201210996

Header: <random>

Namespace: std

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