private void GammaEstimator(double shape, double rate)
{
Rand.Restart(12347);
Gamma g = Gamma.FromShapeAndRate(shape, rate);
Estimator <Gamma> ge = EstimatorFactory.Instance.CreateEstimator <Gamma, double>(Gamma.Uniform());
Accumulator <double> gea = ge as Accumulator <double>;
for (int i = 0; i < 10000; i++)
{
double d = g.Sample();
gea.Add(d);
}
Gamma gest = Gamma.Uniform();
gest = ge.GetDistribution(gest);
double expectedMean = g.GetMean();
double expectedSDev = System.Math.Sqrt(g.GetVariance());
double estimatedMean = gest.GetMean();
double estimatedSDev = System.Math.Sqrt(gest.GetVariance());
Assert.True(System.Math.Abs(expectedMean - estimatedMean) < 0.02);
Assert.True(System.Math.Abs(expectedSDev - estimatedSDev) < 0.02);
}
private void GaussianEstimator(double mean, double precision)
{
Rand.Restart(12347);
Gaussian g = Gaussian.FromMeanAndPrecision(mean, precision);
Estimator <Gaussian> ge = EstimatorFactory.Instance.CreateEstimator <Gaussian, double>(Gaussian.Uniform());
Accumulator <double> gea = ge as Accumulator <double>;
for (int i = 0; i < 10000; i++)
{
gea.Add(g.Sample());
}
Gaussian gest = Gaussian.Uniform();
gest = ge.GetDistribution(gest);
double expectedMean = g.GetMean();
double expectedSDev = System.Math.Sqrt(g.GetVariance());
double estimatedMean = gest.GetMean();
double estimatedSDev = System.Math.Sqrt(gest.GetVariance());
Assert.True(System.Math.Abs(expectedMean - estimatedMean) < 0.02);
Assert.True(System.Math.Abs(expectedSDev - estimatedSDev) < 0.02);
}
/// <summary>
/// Get the estimated distribution from the samples
/// </summary>
/// <param name="result"></param>
/// <returns></returns>
public TDist GetDistribution(TDist result)
{
return(estimator.GetDistribution(result));
}