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;
};

Parameters

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

Remarks

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.

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.

Example

// 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);
}

Output

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

Requirements

Header: <random>

Namespace: std

See Also

Reference

<random>