private static RealVector GenerateRandomInSpere(RealVector currentPoint, double radius, Area area)
{
RealVector normallyDistributed = gorn.GetNormalVector(area.ToDictionary(kvp => kvp.Key, kvp => Tuple.Create(0.0, 1.0)));
var r = Math.Sqrt(normallyDistributed.Elements.Values.Select(v => v * v).Sum());
RealVector shift = (normallyDistributed * (gorn.GetContinuousUniform(0.0, radius) / r)).Elements;
RealVector generatedPoint = (currentPoint + shift).Elements;
return(generatedPoint.Constrain(area));
}
static void TestNormalAndStatistic()
{
var mu_sigma_x = Tuple.Create(17.0, 7.0);
var mu_sigma_y = Tuple.Create(7.0, 17.0);
var prob = new Dictionary <string, Tuple <double, double> >
{
{ "x", mu_sigma_x },
{ "y", mu_sigma_y }
};
int maxAttempts = 10;
bool result = false;
for (int i = 0; i < maxAttempts && !result; ++i)
{
var samples = new List <Dictionary <string, double> >();
for (int j = 0; j < N; ++j)
{
samples.Add(gorn.GetNormalVector(prob));
}
var x = samples.Select(kvp => kvp["x"]);
var y = samples.Select(kvp => kvp["y"]);
var muEst_x = Statistics.GetMean(x);
var sigmaEst_x = Statistics.GetUnbiasedSigma(x);
var muEst_y = Statistics.GetMean(y);
var sigmaEst_y = Statistics.GetUnbiasedSigma(y);
var success_mu_x = Math.Abs(mu_sigma_x.Item1 - muEst_x) < eps;
var success_mu_y = Math.Abs(mu_sigma_y.Item1 - muEst_y) < eps;
var success_sigma_x = Math.Abs(mu_sigma_x.Item2 - sigmaEst_x) < eps;
var success_sigma_y = Math.Abs(mu_sigma_y.Item2 - sigmaEst_y) < eps;
result = success_mu_x && success_mu_y && success_sigma_x && success_sigma_y;
}
Assert.True(result);
}
