public static void MaxOfOthers_MonteCarlo(IList <Gaussian> array, IList <Gaussian> result)
{
if (array.Count == 0)
{
return;
}
if (array.Count == 1)
{
result[0] = Gaussian.Uniform();
return;
}
int iterCount = 1000000;
double[] x = new double[array.Count];
var est = new ArrayEstimator <GaussianEstimator, IList <Gaussian>, Gaussian, double>(array.Count, i => new GaussianEstimator());
for (int iter = 0; iter < iterCount; iter++)
{
for (int i = 0; i < x.Length; i++)
{
x[i] = array[i].Sample();
}
double[] maxOfOthers = Factor.MaxOfOthers(x);
est.Add(maxOfOthers);
}
est.GetDistribution(result);
}
public void ArrayEstimatorTest()
{
int length = 3;
GaussianArray garray = new GaussianArray(length, i => new Gaussian(i, i + 1));
ArrayEstimator <GaussianEstimator, GaussianArray, Gaussian, double> est =
new ArrayEstimator <GaussianEstimator, GaussianArray, Gaussian, double>(
Utilities.Util.ArrayInit(length, i => new GaussianEstimator()));
double[] sum = new double[length];
double[] sum2 = new double[length];
int count = 5;
for (int nSamp = 0; nSamp < count; nSamp++)
{
double[] sample = garray.Sample();
est.Add(sample);
for (int i = 0; i < length; i++)
{
sum[i] += sample[i];
sum2[i] += sample[i] * sample[i];
}
}
GaussianArray expected = new GaussianArray(length, delegate(int i)
{
double m = sum[i] / count;
double v = sum2[i] / count - m * m;
return(new Gaussian(m, v));
});
GaussianArray actual = new GaussianArray(length);
actual = est.GetDistribution(actual);
Console.WriteLine(Utilities.StringUtil.JoinColumns("result = ", actual, " should be ", expected));
Assert.True(expected.MaxDiff(actual) < 1e-10);
}