binomial_distribution 類別
產生二項式分佈。
template<class IntType = int> class binomial_distribution { public: // types typedef IntType result_type; struct param_type; // constructors and reset functions explicit binomial_distribution(IntType t = 1, double p = 0.5); explicit 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 t() const; double p() const; param_type param() const; void param(const param_type& parm); result_type min() const; result_type max() const; };
參數
- IntType
整數結果類型,預設值為 int。 如需可能的類型,請參閱 <random>。
備註
此範本類別描述產生使用者指定之整數類型的值的分佈 (若無提供則為 int 類型),而這是根據二項式分佈離散可能性函式進行分佈。 下表提供各個成員的文章連結。
binomial_distribution::t |
binomial_distribution::param |
|
binomial_distribution::operator() |
binomial_distribution::p |
屬性成員 t() 和 p() 會分別傳回目前儲存的分佈參數值 t 和 p。
如需分佈類別及其成員的詳細資訊,請參閱 <random>。
如需二項式分佈離散可能性函式的詳細資訊,請參閱 Wolfram MathWorld 文章:二項式分佈 (英文)。
範例
// compile with: /EHsc /W4
#include <random>
#include <iostream>
#include <iomanip>
#include <string>
#include <map>
void test(const int t, 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::binomial_distribution<> distr(t, p);
std::cout << std::endl;
std::cout << "p == " << distr.p() << std::endl;
std::cout << "t == " << distr.t() << 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 t_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 t distribution (where 0 <= t): ";
std::cin >> t_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(t_dist, p_dist, samples);
}
輸出
第一次執行:
Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for t distribution (where 0 <= t): 22
Enter a double value for p distribution (where 0.0 <= p <= 1.0): .25
Enter an integer value for a sample count: 100
p == 0.25
t == 22
Histogram for 100 samples:
1 :
2 ::
3 :::::::::::::
4 ::::::::::::::
5 :::::::::::::::::::::::::
6 ::::::::::::::::::
7 :::::::::::::
8 ::::::
9 ::::::
11 :
12 :
第二次執行:
Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for t distribution (where 0 <= t): 22
Enter a double value for p distribution (where 0.0 <= p <= 1.0): .5
Enter an integer value for a sample count: 100
p == 0.5
t == 22
Histogram for 100 samples:
6 :
7 ::
8 :::::::::
9 ::::::::::
10 ::::::::::::::::
11 :::::::::::::::::::
12 :::::::::::
13 :::::::::::::
14 :::::::::::::::
15 ::
16 ::
第三次執行:
Use CTRL-Z to bypass data entry and run using default values.
Enter an integer value for t distribution (where 0 <= t): 22
Enter a double value for p distribution (where 0.0 <= p <= 1.0): .75
Enter an integer value for a sample count: 100
p == 0.75
t == 22
Histogram for 100 samples:
13 ::::
14 :::::::::::
15 :::::::::::::::
16 :::::::::::::::::::::
17 ::::::::::::::
18 :::::::::::::::::
19 :::::::::::
20 ::::::
21 :
需求
標頭:<random>
命名空間: std