piecewise_linear_distribution 類別

產生分段線性分佈，其中有不同寬度間隔，且每個間隔中有線性變化可能性。

template<class RealType = double> class piecewise_linear_distribution { public:     // types     typedef RealType result_type;     struct param_type;     // constructor and reset functions     piecewise_linear_distribution();     template<class InputIteratorI, class InputIteratorW>     piecewise_linear_distribution(InputIteratorI firstI, InputIteratorI lastI, InputIteratorW firstW);     template<class UnaryOperation>     piecewise_linear_distribution(initializer_list<RealType> intervals, UnaryOperation weightfunc);     template<class UnaryOperation>     piecewise_linear_distribution(size_t count, RealType xmin, RealType xmax, UnaryOperation weightfunc);     explicit piecewise_linear_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     vector<result_type> intervals() const;     vector<result_type> densities() const;     param_type param() const;     void param(const param_type& parm);     result_type min() const;     result_type max() const; };

參數

• RealType
  浮點結果類型，預設值為 double。 如需可能的類型，請參閱 <random>。

備註

此取樣分佈有不同寬度間隔，且每個間隔中有線性變動可能性。 如需其他取樣分佈的詳細資訊，請參閱 piecewise_constant_distribution 和 discrete_distribution。

下表提供各個成員的文章連結：

piecewise_linear_distribution::piecewise_linear_distribution	piecewise_linear_distribution::intervals	piecewise_linear_distribution::param
piecewise_linear_distribution::operator()	piecewise_linear_distribution::densities	piecewise_linear_distribution::param_type

屬性函式 intervals() 會傳回 vector<RealType>，其中具有儲存的分佈間隔的設定。

屬性函式 densities() 會傳回 vector<RealType>，其中具有針對每個間隔設定所儲存的密度，這是根據建構函式參數中提供的加權所計算而得。

如需分佈類別及其成員的詳細資訊，請參閱 <random>。

範例

 

// compile with: /EHsc /W4
#include <random> 
#include <iostream>
#include <iomanip>
#include <string>
#include <map>

using namespace std;

void test(const int s) {

    // uncomment to use a non-deterministic generator
    // random_device rd;
    // mt19937 gen(rd());
    mt19937 gen(1701);

    // Three intervals, non-uniform: 0 to 1, 1 to 6, and 6 to 15
    vector<double> intervals{ 0, 1, 6, 15 };
    // weights determine the densities used by the distribution
    vector<double> weights{ 1, 5, 5, 10 };

    piecewise_linear_distribution<double> distr(intervals.begin(), intervals.end(), weights.begin());

    cout << endl;
    cout << "min() == " << distr.min() << endl;
    cout << "max() == " << distr.max() << endl;
    cout << "intervals (index: interval):" << endl;
    vector<double> i = distr.intervals();
    int counter = 0;
    for (const auto& n : i) {
        cout << fixed << setw(11) << counter << ": " << setw(14) << setprecision(10) << n << endl;
        ++counter;
    }
    cout << endl;
    cout << "densities (index: density):" << endl;
    vector<double> d = distr.densities();
    counter = 0;
    for (const auto& n : d) {
        cout << fixed << setw(11) << counter << ": " << setw(14) << setprecision(10) << n << endl;
        ++counter;
    }
    cout << endl;

    // generate the distribution as a histogram
    map<int, int> histogram;
    for (int i = 0; i < s; ++i) {
        ++histogram[distr(gen)];
    }

    // print results
    cout << "Distribution for " << s << " samples:" << endl;
    for (const auto& elem : histogram) {
        cout << setw(5) << elem.first << '-' << elem.first + 1 << ' ' << string(elem.second, ':') << endl;
    }
    cout << endl;
}

int main()
{
    int samples = 100;

    cout << "Use CTRL-Z to bypass data entry and run using default values." << endl;
    cout << "Enter an integer value for the sample count: ";
    cin >> samples;

    test(samples);
}

輸出

需求

標頭：<random>

命名空間: std

請參閱

參考

<random>