poisson_distribution Class
Generates a Poisson distribution.
Syntax
template<class IntType = int>
class poisson_distribution
{
public:
// types
typedef IntType result_type;
struct param_type;
// constructors and reset functions
explicit poisson_distribution(double mean = 1.0);
explicit poisson_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
double mean() 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 class template describes a distribution that produces values of a user-specified integral type with a Poisson distribution. The following table links to articles about individual members.
poisson_distribution
param_type
The property function mean() returns the value for stored distribution parameter mean.
The property member param() sets or returns the param_type stored distribution parameter package.
The min() and max() member functions return the smallest possible result and largest possible result, respectively.
The reset() member function discards any cached values, so that the result of the next call to operator() does not depend on any values obtained from the engine before the call.
The operator() member functions return the next generated value based on the URNG engine, either from the current parameter package, or the specified parameter package.
For more information about distribution classes and their members, see <random>.
For detailed information about the Poisson distribution, see the Wolfram MathWorld article Poisson Distribution.
Example
// compile with: /EHsc /W4
#include <random>
#include <iostream>
#include <iomanip>
#include <string>
#include <map>
void test(const double p, const int s) {
// uncomment to use a non-deterministic generator
// std::random_device gen;
std::mt19937 gen(1701);
std::poisson_distribution<> distr(p);
std::cout << std::endl;
std::cout << "min() == " << distr.min() << std::endl;
std::cout << "max() == " << distr.max() << std::endl;
std::cout << "p() == " << std::fixed << std::setw(11) << std::setprecision(10) << distr.mean() << 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 << "Distribution 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()
{
double p_dist = 1.0;
int samples = 100;
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 'mean' distribution parameter (must be greater than zero): ";
std::cin >> p_dist;
std::cout << "Enter an integer value for the sample count: ";
std::cin >> samples;
test(p_dist, samples);
}
First test:
Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'mean' distribution parameter (must be greater than zero): 1
Enter an integer value for the sample count: 100
min() == 0
max() == 2147483647
p() == 1.0000000000
Distribution for 100 samples:
0 ::::::::::::::::::::::::::::::
1 ::::::::::::::::::::::::::::::::::::::
2 :::::::::::::::::::::::
3 ::::::::
5 :
Second test:
Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'mean' distribution parameter (must be greater than zero): 10
Enter an integer value for the sample count: 100
min() == 0
max() == 2147483647
p() == 10.0000000000
Distribution for 100 samples:
3 :
4 ::
5 ::
6 ::::::::
7 ::::
8 ::::::::
9 ::::::::::::::
10 ::::::::::::
11 ::::::::::::::::
12 :::::::::::::::
13 ::::::::
14 ::::::
15 :
16 ::
17 :
Requirements
Header: <random>
Namespace: std
poisson_distribution::poisson_distribution
Constructs the distribution.
explicit poisson_distribution(RealType mean = 1.0);
explicit binomial_distribution(const param_type& parm);
Parameters
mean
The mean distribution parameter.
parm
The parameter structure used to construct the distribution.
Remarks
Precondition: 0.0 < mean
The first constructor constructs an object whose stored mean value holds the value mean.
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.
poisson_distribution::param_type
Stores the parameters of the distribution.
struct param_type {
typedef poisson_distribution<IntType> distribution_type;
param_type(double mean = 1.0);
double mean() const;
bool operator==(const param_type& right) const;
bool operator!=(const param_type& right) const;
};
Parameters
See constructor parameters for poisson_distribution.
Remarks
Precondition: 0.0 < mean
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.