SimpleRNG.hpp

 
 
#ifndef SIMPLE_RNG_HPP
#define SIMPLE_RNG_HPP
 
#include <string>
#include <cmath>
 
class SimpleRNG
{
private:
    unsigned int m_w;
    unsigned int m_z;
 
    // This is the heart of the generator.
    // It uses George Marsaglia's MWC algorithm to produce an unsigned integer.
    // See http://www.bobwheeler.com/statistics/Password/MarsagliaPost.txt
    unsigned int GetUint()
    {
        m_z = 36969 * (m_z & 65535) + (m_z >> 16);
        m_w = 18000 * (m_w & 65535) + (m_w >> 16);
        return (m_z << 16) + m_w;
    }
 
public:
 
    SimpleRNG()
    {
        // These values are not magical, just the default values Marsaglia used.
        // Any pair of unsigned integers should be fine.
        m_w = 521288629;
        m_z = 362436069;
    }
 
    // The random generator seed can be set three ways:
    // 1) specifying two non-zero unsigned integers
    // 2) specifying one non-zero unsigned integer and taking a default value for the second
    // 3) setting the seed from the system time
 
    void SetSeed(unsigned int u, unsigned int v)
    {
        if (u != 0) m_w = u;
        if (v != 0) m_z = v;
    }
 
    void SetSeed(unsigned int u)
    {
        m_w = u;
    }
 
    void SetSeedFromSystemTime()
    {
        long x = clock();
        SetSeed((unsigned int)(x >> 16), (unsigned int)(x % 4294967296));
    }
 
    // Produce a uniform random sample from the open interval (0, 1).
    // The method will not return either end point.
    double GetUniform()
    {
        // 0 <= u < 2^32
        unsigned int u = GetUint();
        // The magic number below is 1/(2^32 + 2).
        // The result is strictly between 0 and 1.
        return (u + 1.0) * 2.328306435454494e-10;
    }
 
    // Get normal (Gaussian) random sample with mean 0 and standard deviation 1
    double GetNormal()
    {
        // Use Box-Muller algorithm
        double u1 = GetUniform();
        double u2 = GetUniform();
        double r = std::sqrt( -2.0*std::log(u1) );
        double theta = 2.0*3.14159265359*u2;
        return r*std::sin(theta);
    }
 
    // Get normal (Gaussian) random sample with specified mean and standard deviation
    double GetNormal(double mean, double standardDeviation)
    {
        if (standardDeviation <= 0.0)
        {
            std::cerr << __FILE__ << ":" << __LINE__ << " Shape must be positive. Received." << std::to_string(standardDeviation);
            throw 100001;
        }
        return mean + standardDeviation*GetNormal();
    }
 
    // Get exponential random sample with mean 1
    double GetExponential()
    {
        return -std::log( GetUniform() );
    }
 
    // Get exponential random sample with specified mean
    double GetExponential(double mean)
    {
        if (mean <= 0.0)
        {
            std::cerr << __FILE__ << ":" << __LINE__ << "Mean must be positive. Received " << mean;
            throw 100002;
        }
        return mean*GetExponential();
    }
 
    double GetGamma(double shape, double scale)
    {
        // Implementation based on "A Simple Method for Generating Gamma Variables"
        // by George Marsaglia and Wai Wan Tsang.  ACM Transactions on Mathematical Software
        // Vol 26, No 3, September 2000, pages 363-372.
 
        double d, c, x, xsquared, v, u;
 
        if (shape >= 1.0)
        {
            d = shape - 1.0/3.0;
            c = 1.0/std::sqrt(9.0*d);
            for (;;)
            {
                do
                {
                    x = GetNormal();
                    v = 1.0 + c*x;
                }
                while (v <= 0.0);
                v = v*v*v;
                u = GetUniform();
                xsquared = x*x;
                if (u < 1.0 -.0331*xsquared*xsquared || std::log(u) < 0.5*xsquared + d*(1.0 - v + std::log(v)))
                    return scale*d*v;
            }
        }
        else if (shape <= 0.0)
        {
            std::cerr << __FILE__ << ":" << __LINE__ << " Shape must be positive. Received" << shape << "\n";
            throw 100003;
        }
        else
        {
            double g = GetGamma(shape+1.0, 1.0);
            double w = GetUniform();
            return scale*g*std::pow(w, 1.0/shape);
        }
    }
 
    double GetChiSquare(double degreesOfFreedom)
    {
        // A chi squared distribution with n degrees of freedom
        // is a gamma distribution with shape n/2 and scale 2.
        return GetGamma(0.5 * degreesOfFreedom, 2.0);
    }
 
    double GetInverseGamma(double shape, double scale)
    {
        // If X is gamma(shape, scale) then
        // 1/Y is inverse gamma(shape, 1/scale)
        return 1.0 / GetGamma(shape, 1.0 / scale);
    }
 
    double GetWeibull(double shape, double scale)
    {
        if (shape <= 0.0 || scale <= 0.0)
        {
            std::cerr << __FILE__ << ":" << __LINE__ << " Shape and scale parameters must be positive. Recieved " << shape << " and " << scale;
            throw 100004;
        }
        return scale * std::pow(-std::log(GetUniform()), 1.0 / shape);
    }
 
    double GetCauchy(double median, double scale)
    {
        if (scale <= 0)
        {
            std::cerr << __FILE__ << ":" << __LINE__ << "Scale must be positive. Received " << scale << "\n";
            throw 100005;
        }
 
        double p = GetUniform();
 
        // Apply inverse of the Cauchy distribution function to a uniform
        return median + scale*std::tan(3.14159265359*(p - 0.5));
    }
 
    double GetStudentT(double degreesOfFreedom)
    {
        if (degreesOfFreedom <= 0)
        {
            std::cerr << __FILE__ << ":" << __LINE__ << " Degrees of freedom must be positive. Received " << degreesOfFreedom;
            throw 100006;
        }
 
        // See Seminumerical Algorithms by Knuth
        double y1 = GetNormal();
        double y2 = GetChiSquare(degreesOfFreedom);
        return y1 / std::sqrt(y2 / degreesOfFreedom);
    }
 
    // The Laplace distribution is also known as the double exponential distribution.
    double GetLaplace(double mean, double scale)
    {
        double u = GetUniform();
        return (u < 0.5) ?
            mean + scale*std::log(2.0*u) :
            mean - scale*std::log(2*(1-u));
    }
 
    double GetLogNormal(double mu, double sigma)
    {
        return std::exp(GetNormal(mu, sigma));
    }
 
    double GetBeta(double a, double b)
    {
        if (a <= 0.0 || b <= 0.0)
        {
            std::cerr << __FILE__ << ":" << __LINE__ << "Beta parameters must be positive. Received " << a << " and " << b << "\n";
            throw 100007;
        }
 
        // There are more efficient methods for generating beta samples.
        // However such methods are a little more efficient and much more complicated.
        // For an explanation of why the following method works, see
        // http://www.johndcook.com/distribution_chart.html#gamma_beta
 
        double u = GetGamma(a, 1.0);
        double v = GetGamma(b, 1.0);
        return u / (u + v);
    }
};
 
#endif