weibull_distribution-Klasse
Generiert eine Weibull-Verteilung.
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; };
Parameter
- RealType
Der Gleitkommaergebnistyp. Der Standardwert ist double. Die möglichen Typen finden Sie unter <random>.
Hinweise
Die Vorlagenklasse beschreibt eine Verteilung, die Werte eines benutzerdefinierten ganzzahligen Typs produziert. Wenn kein entsprechend der Weibull-Verteilung verteilter Wert ausgeben wird, geben Sie double ein. Die folgende Tabelle ist mit Artikeln über einzelne Member verknüpft.
weibull_distribution::a |
weibull_distribution::param |
|
weibull_distribution::operator() |
weibull_distribution::b |
Die Eigenschaftsfunktionen a() und b() geben ihre entsprechenden Werte für die gespeicherten Verteilungsparameter a und b zurück.
Weitere Informationen zu Verteilungsklassen und ihren Membern finden Sie unter <random>.
Ausführliche Informationen über die Weibull-Verteilung finden Sie im Wolfram MathWorld-Artikel Weibull Distribution.
Beispiel
// 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);
}
Ausgabe
Erste Ausführung:
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
Zweite Ausführung:
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
Anforderungen
Header: <random>
Namespace: std