// Thanks to John D. Cook (johndcook.com) for this public domain random variate code
// Get normal (Gaussian) random sample with specified mean and standard deviation
static double GetNormal(double mean = 0.0, double standardDeviation = 1.0)
{
if (standardDeviation <= 0.0)
{
StnSciScenario.LogWarning("Invalid standard deviation: " + standardDeviation);
return(0);
}
// Use Box-Muller algorithm
double u1 = GetUniform();
double u2 = GetUniform();
double r = Math.Sqrt(-2.0 * Math.Log(u1));
double theta = 2.0 * Math.PI * u2;
return(mean + standardDeviation * r * Math.Sin(theta));
}
static 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 / Math.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 || Math.Log(u) < 0.5 * xsquared + d * (1.0 - v + Math.Log(v)))
{
return(scale * d * v);
}
}
}
else if (shape <= 0.0)
{
StnSciScenario.LogWarning("Invalid Gamma shape: " + shape);
return(0);
}
else
{
double g = GetGamma(shape + 1.0, 1.0);
double w = GetUniform();
return(scale * g * Math.Pow(w, 1.0 / shape));
}
}
