public void NextUInt10M()
{
for (int i = 0; i < __loops; i++)
{
_rng.NextUInt();
}
}
public void NextUInt()
{
int sampleCount = 10000000;
XorShiftRandom rng = new XorShiftRandom();
double[] sampleArr = new double[sampleCount];
for (int i = 0; i < sampleCount; i++)
{
sampleArr[i] = rng.NextUInt();
}
UniformDistributionTest(sampleArr, 0.0, uint.MaxValue);
}
/// <summary>
/// Take a sample from the standard gaussian distribution, i.e. with mean of 0 and standard deviation of 1.
/// </summary>
public double SampleStandard()
{
for (; ;)
{
// Select box at random.
byte u = _rng.NextByte();
int i = (int)(u & 0x7F);
double sign = ((u & 0x80) == 0) ? -1.0 : 1.0;
// Generate uniform random value with range [0,0xffffffff].
uint u2 = _rng.NextUInt();
// Special case for the base segment.
if (0 == i)
{
if (u2 < _xComp[0])
{ // Generated x is within R0.
return(u2 * __UIntToU * _A_Div_Y0 * sign);
}
// Generated x is in the tail of the distribution.
return(SampleTail() * sign);
}
// All other segments.
if (u2 < _xComp[i])
{ // Generated x is within the rectangle.
return(u2 * __UIntToU * _x[i] * sign);
}
// Generated x is outside of the rectangle.
// Generate a random y coordinate and test if our (x,y) is within the distribution curve.
// This execution path is relatively slow/expensive (makes a call to Math.Exp()) but relatively rarely executed,
// although more often than the 'tail' path (above).
double x = u2 * __UIntToU * _x[i];
if (_y[i - 1] + ((_y[i] - _y[i - 1]) * _rng.NextDouble()) < GaussianPdfDenorm(x))
{
return(x * sign);
}
}
}