weibull_distribution Class

Generates a Weibull distribution.

template<class RealType = double>
class weibull_distribution
{
public:
    // types
    typedef RealType result_type;
    struct param_type;
    // constructor and reset functions
    explicit weibull_distribution(RealType a = 1.0, RealType b = 1.0);
    explicit weibull_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 a() const;
    RealType 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 a distribution that produces values of a user-specified integral type, or type double if none is provided, distributed according to the Weibull Distribution. The following table links to articles about individual members.

The property functions a() and b() return their respective values for stored distribution parameters a and b.

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

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

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 generator
    //    std::random_device gen;
    std::mt19937 gen(1701);

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

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

int main()
{
    double a_dist = 0.0;
    double b_dist = 1;

    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 'a' distribution parameter (must be greater than zero): ";
    std::cin >> a_dist;
    std::cout << "Enter a floating point value for the 'b' distribution parameter (must be greater than zero): ";
    std::cin >> b_dist;
    std::cout << "Enter an integer value for the sample count: ";
    std::cin >> samples;

    test(a_dist, b_dist, samples);
}

Output

First run:

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

min() == 0
max() == 1.79769e+308
a() == 1.0000000000
b() == 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

Second run:

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'a' distribution parameter (must be greater than zero): .5
Enter a floating point value for the 'b' distribution parameter (must be greater than zero): 5.5
Enter an integer value for the sample count: 10

min() == 0
max() == 1.79769e+308
a() == 0.5000000000
b() == 5.5000000000
Distribution for 10 samples:
          1:   0.0482759823
          2:   0.0826617486
          3:   2.2835941207
          4:   2.3604817485
          5:   2.9417663742
          6:   2.9418471657
          7:   3.1685268104
          8:  11.5109922290
          9:  14.8543594043
         10:  24.7220241239

Requirements

Header: <random>

Namespace: std

See Also

Reference

<random>