weibull_distribution Class

 

For the latest documentation on Visual Studio 2017, see Visual Studio 2017 Documentation.

For the latest documentation on Visual Studio 2017, see weibull_distribution Class on docs.microsoft.com. Generates a Weibull distribution.

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>.

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_distributionweibull_distribution::aweibull_distribution::param
weibull_distribution::operator()weibull_distribution::bweibull_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.

// 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);  
}  
  

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  

Header: <random>

Namespace: std

explicit weibull_distribution(RealType a = 1.0, RealType b = 1.0);

 
explicit weibull_distribution(const param_type& parm);

Parameters

a
The a distribution parameter.

b
The b distribution parameter.

parm
The parameter structure used to construct the distribution.

Remarks

Precondition: 0.0 < a and 0.0 < b

The first constructor constructs an object whose stored avalue 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.

Stores the parameters of the distribution.

struct param_type {  
   typedef weibull_distribution<RealType> distribution_type;  
   param_type(RealType a = 1.0, 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 weibull_distribution Class.

Remarks

Precondition: 0.0 < a and 0.0 < 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.

<random>

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