negative_binomial_distribution Class
Generates a negative binomial distribution.
template<class IntType = int>
class negative_binomial_distribution
{
public:
// types
typedef IntType result_type;
struct param_type;
// constructor and reset functions
explicit negative_binomial_distribution(IntType k = 1, double p = 0.5);
explicit negative_binomial_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
IntType k() const;
double p() const;
param_type param() const;
void param(const param_type& parm);
result_type min() const;
result_type max() const;
};
Parameters
- IntType
The integer result type, defaults to int. For possible types, see <random>.
Remarks
The template class describes a distribution that produces values of a user-specified integral type, or type int if none is provided, distributed according to the Negative Binomial Distribution discrete probability function. The following table links to articles about individual members.
negative_binomial_distribution::negative_binomial_distribution |
negative_binomial_distribution::k |
negative_binomial_distribution::param |
negative_binomial_distribution::operator() |
negative_binomial_distribution::p |
The property members k() and p() return the currently stored distribution parameter values k and p respectively.
For more information about distribution classes and their members, see <random>.
For detailed information about the negative binomial distribution discrete probability function, see the Wolfram MathWorld article Negative Binomial Distribution.
Example
// compile with: /EHsc /W4
#include <random>
#include <iostream>
#include <iomanip>
#include <string>
#include <map>
void test(const int k, const double p, const int& s) {
// uncomment to use a non-deterministic seed
// std::random_device rd;
// std::mt19937 gen(rd());
std::mt19937 gen(1729);
std::negative_binomial_distribution<> distr(k, p);
std::cout << std::endl;
std::cout << "k == " << distr.k() << std::endl;
std::cout << "p == " << distr.p() << std::endl;
// generate the distribution as a histogram
std::map<int, int> histogram;
for (int i = 0; i < s; ++i) {
++histogram[distr(gen)];
}
// print results
std::cout << "Histogram for " << s << " samples:" << std::endl;
for (const auto& elem : histogram) {
std::cout << std::setw(5) << elem.first << ' ' << std::string(elem.second, ':') << std::endl;
}
std::cout << std::endl;
}
int main()
{
int k_dist = 1;
double p_dist = 0.5;
int samples = 100;
std::cout << "Use CTRL-Z to bypass data entry and run using default values." << std::endl;
std::cout << "Enter an integer value for k distribution (where 0 < k): ";
std::cin >> k_dist;
std::cout << "Enter a double value for p distribution (where 0.0 < p <= 1.0): ";
std::cin >> p_dist;
std::cout << "Enter an integer value for a sample count: ";
std::cin >> samples;
test(k_dist, p_dist, samples);
}
Output
First run:
Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for k distribution (where 0 < k): 1
Enter a double value for p distribution (where 0.0 < p <= 1.0): .5
Enter an integer value for a sample count: 100
k == 1
p == 0.5
Histogram for 100 samples:
0 :::::::::::::::::::::::::::::::::::::::::::
1 ::::::::::::::::::::::::::::::::
2 ::::::::::::
3 :::::::
4 ::::
5 ::
Second run:
Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for k distribution (where 0 < k): 100
Enter a double value for p distribution (where 0.0 < p <= 1.0): .667
Enter an integer value for a sample count: 100
k == 100
p == 0.667
Histogram for 100 samples:
31 ::
32 :
33 ::
34 :
35 ::
37 ::
38 :
39 :
40 ::
41 :::
42 :::
43 :::::
44 :::::
45 ::::
46 ::::::
47 ::::::::
48 :::
49 :::
50 :::::::::
51 :::::::
52 ::
53 :::
54 :::::
56 ::::
58 :
59 :::::
60 ::
61 :
62 ::
64 :
69 ::::
Requirements
Header: <random>
Namespace: std