/// <summary>
/// Returns a Gaussian distributed double.
/// </summary>
public double GetDouble()
{
double value;
if (!Double.IsNaN(m_cachedValue))
{
value = m_cachedValue;
m_cachedValue = Double.NaN;
}
else
{
// using the polar form of the Box-Muller transformation to
// transform two random uniform values to two Gaussian
// distributed values
double x1, x2, w;
do
{
x1 = 2.0 * m_rndUniform.UniformDouble() - 1.0;
x2 = 2.0 * m_rndUniform.UniformDouble() - 1.0;
w = x1 * x1 + x2 * x2;
} while (w >= 1.0);
w = Fun.Sqrt((-2.0 * Fun.Log(w)) / w);
value = x1 * w;
m_cachedValue = x2 * w;
}
return(value);
}
/// <summary>
/// Generates normal distributed random variable with given mean and standard deviation.
/// Uses the Box-Muller Transformation to transform two uniform distributed random variables to one normal distributed value.
/// NOTE: If multiple normal distributed random values are required, consider using <see cref="RandomGaussian"/>.
/// </summary>
public static double Gaussian(this IRandomUniform rnd, double mean = 0.0, double stdDev = 1.0)
{
// Box-Muller Transformation
var u1 = 1.0 - rnd.UniformDouble(); // uniform (0,1] -> log requires > 0
var u2 = rnd.UniformDouble(); // uniform [0,1)
var randStdNormal = Fun.Sqrt(-2.0 * Fun.Log(u1)) *
Fun.Sin(Constant.PiTimesTwo * u2);
return(mean + stdDev * randStdNormal);
}
