/// <summary>
/// Sample from the distribution tail (defined as having x >= __R).
/// </summary>
/// <returns></returns>
private double SampleTail()
{
double x, y;
do
{
// Note. we use NextDoubleNonZero() because Log(0) returns NaN and will also tend to be a very slow execution path (when it occurs, which is rarely).
x = -Math.Log(_rng.NextDoubleNonZero()) / __R;
y = -Math.Log(_rng.NextDoubleNonZero());
}while (y + y < x * x);
return(__R + x);
}
/// <summary>
/// Return a random double in the range [min,max]
/// </summary>
/// <param name="min">Inclusive minimum of range</param>
/// <param name="max">Inclusive maximum of range</param>
/// <returns></returns>
public double Inclusive(double min, double max)
{
if (min >= max)
{
throw new ArgumentOutOfRangeException($"Min [{min}] must be < Max [{max}]!");
}
return(_fastRandom.NextDoubleNonZero() * (max + double.Epsilon - min) + min);
//if (min >= max) throw new ArgumentException($"Min [{min}] must be < Max [{max}]!");
//var random1 = _fastRandom.NextDouble(); // random1 is in range [0,1)
//var random2 = _fastRandom.NextDouble() * -1.0 + 1.0; // random2 is in range (0,1]
//// random1 + random2 is in range (0,2), divide by 2 (multiply by 0.5) for range (0,1)
//return (random1 + random2) * (max - min) * (0.5) + min;
}
public void NextDoubleNonZero10M()
{
for (int i = 0; i < __loops; i++)
{
_rng.NextDoubleNonZero();
}
}
public void NextDoubleNonZero()
{
int sampleCount = 10000000;
XorShiftRandom rng = new XorShiftRandom();
double[] sampleArr = new double[sampleCount];
for (int i = 0; i < sampleCount; i++)
{
sampleArr[i] = rng.NextDoubleNonZero();
if (0.0 == sampleArr[i])
{
Assert.Fail();
}
}
UniformDistributionTest(sampleArr, 0.0, 1.0);
}