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.

weibull_distribution::weibull_distribution	weibull_distribution::a	weibull_distribution::param
weibull_distribution::operator()	weibull_distribution::b	weibull_distribution::param_type

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>