NextDouble()
{
double randTheta = _uniformDistribution.NextDouble();
double randW = _exponentialDistribution.NextDouble();
if (!Number.AlmostEqual(_exponent, 1))
{
double angle = _exponent * (randTheta + _theta);
double factor1 = Math.Sin(angle) / Math.Pow(Math.Cos(randTheta), (1.0 / _exponent));
double factor2 = Math.Pow(Math.Cos(randTheta - angle) / randW, (1 - _exponent) / _exponent);
return(_factor * factor1 * factor2);
}
double part1 = Constants.Pi_2 + (_skewness * randTheta);
double summand = part1 * Math.Tan(randTheta);
double subtrahend = _skewness * Math.Log(Constants.Pi_2 * randW * Math.Cos(randTheta) / part1);
return((2.0 / Math.PI) * (summand - subtrahend));
}
static void BuildLinearCase(int start, int stop, out double[] x, out double[] y, out double[] xtest, out double[] ytest)
{
const double yOffset = 2.0;
int samples = stop - start + 1;
ContinuousUniformDistribution uniform = new ContinuousUniformDistribution();
// build linear samples
x = new double[samples];
y = new double[samples];
for(int i = 0; i < x.Length; i++)
{
int z = start + i;
x[i] = z;
y[i] = z + yOffset; // arbitrary small y-axis offset
}
// build linear test vectors randomly between the sample points
xtest = new double[samples+1];
ytest = new double[samples+1];
if(samples == 1)
{
xtest[0] = start - uniform.NextDouble();
xtest[1] = start + uniform.NextDouble();
ytest[0] = ytest[1] = start + yOffset;
}
else
{
for(int i = 0; i < xtest.Length; i++)
{
double z = (i - 1) + uniform.NextDouble();
xtest[i] = z;
ytest[i] = z + yOffset;
}
}
}
