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lognormal_distribution Class

Generates a log normal distribution.

template<class RealType = double>
class lognormal_distribution
{
public:
    // types
    typedef RealType result_type;
    struct param_type;
    // constructor and reset functions
    explicit lognormal_distribution(RealType m = 0.0, RealType s = 1.0);
    explicit lognormal_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 m() const;
    RealType s() const;
    param_type param() const;
    void param(const param_type& parm);
    result_type min() const;
    result_type max() const;
};

RealType

The floating-point result type, defaults to double. For possible types, see <random>.

The template class describes a distribution that produces values of a user-specified integral type, or type double if none is provided, distributed according to the Log Normal Distribution. The following table links to articles about individual members.

lognormal_distribution::lognormal_distribution

lognormal_distribution::m

lognormal_distribution::param

lognormal_distribution::operator()

lognormal_distribution::s

lognormal_distribution::param_type

The property functions m() and s() return the values for the stored distribution parameters m and s respectively.

For more information about distribution classes and their members, see <random>.

For detailed information about the LogNormal distribution, see the Wolfram MathWorld article LogNormal Distribution.

 

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

using namespace std;

void test(const double m, const double s, const int samples) {

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

    lognormal_distribution<> distr(m, s);

    cout << endl;
    cout << "min() == " << distr.min() << endl;
    cout << "max() == " << distr.max() << endl;
    cout << "m() == " << fixed << setw(11) << setprecision(10) << distr.m() << endl;
    cout << "s() == " << fixed << setw(11) << setprecision(10) << distr.s() << endl;

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

    // print results
    cout << "Distribution for " << samples << " samples:" << endl;
    int counter = 0;
    for (const auto& elem : histogram) {
        cout << fixed << setw(11) << ++counter << ": "
            << setw(14) << setprecision(10) << elem.first << endl;
    }
    cout << endl;
}

int main()
{
    double m_dist = 1;
    double s_dist = 1;
    int samples = 10;

    cout << "Use CTRL-Z to bypass data entry and run using default values." << endl;
    cout << "Enter a floating point value for the 'm' distribution parameter: ";
    cin >> m_dist;
    cout << "Enter a floating point value for the 's' distribution parameter (must be greater than zero): ";
    cin >> s_dist;
    cout << "Enter an integer value for the sample count: ";
    cin >> samples;

    test(m_dist, s_dist, samples);
}

Use CTRL-Z to bypass data entry and run using default values.
Enter a floating point value for the 'm' distribution parameter: 0
Enter a floating point value for the 's' distribution parameter (must be greater than zero): 1
Enter an integer value for the sample count: 10

min() == -1.79769e+308
max() == 1.79769e+308
m() == 0.0000000000
s() == 1.0000000000
Distribution for 10 samples:
          1:   0.3862809339
          2:   0.4128865601
          3:   0.4490576787
          4:   0.4862035428
          5:   0.5930607126
          6:   0.8190778771
          7:   0.8902379317
          8:   2.8332911667
          9:   5.1359445565
         10:   5.4406507912

Header: <random>

Namespace: std

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