private void getResult(ObservablePoint[] ret, GearedValues <ObservablePoint> shortWave)
{
if (ret.Length == 4)
{
t1Ret = (ret[1].X - ret[0].X).ToString("N2") + " ms";
t2Ret = (ret[2].X - ret[1].X).ToString("N2") + " ms";
t3Ret = (ret[3].X - ret[2].X).ToString("N2") + " ms";
t4Ret = (ret[3].X - ret[0].X).ToString("N2") + " ms";
var arr1 = shortWave.Skip((int)(ret[0].X / 300 * 6000)).Take((int)((ret[1].X - ret[0].X) / 300 * 6000)).Select(x => Math.Round(x.Y, 2));
var arr2 = shortWave.Skip((int)(ret[2].X / 300 * 6000)).Take((int)((ret[3].X - ret[2].X) / 300 * 6000)).Select(x => Math.Round(x.Y, 2));
var arr3 = shortWave.Skip((int)(ret[1].X / 300 * 6000)).Take((int)((ret[2].X - ret[1].X) / 300 * 6000)).Select(x => Math.Round(x.Y, 2));
//double r1 = LogNormal.Estimate(arr1).Mean;
//double r2 = LogNormal.Estimate(arr2).Mean;
//double r3 = LogNormal.Estimate(arr3).Mean;
double r1 = GetElevationMode(arr1.ToList());
double r2 = GetElevationMode(arr2.ToList());
//double r3 = GetElevationMode(arr3.ToList());
double r3 = LogNormal.Estimate(arr3).Mean;
//var sss = new MathNet.Numerics.Differentiation.NumericalDerivative(20, 2);
R1Ret = r1.ToString("N2") + " Ω";
R2Ret = r2.ToString("N2") + " Ω";
R1AndR2Ret = r3.ToString("N2") + " Ω";;
}
}
public void CanEstimateParameters(double mu, double sigma)
{
var original = new LogNormal(mu, sigma, new Random(100));
var estimated = LogNormal.Estimate(original.Samples().Take(10000));
AssertHelpers.AlmostEqual(mu, estimated.Mu, 2);
AssertHelpers.AlmostEqual(sigma, estimated.Sigma, 2);
}
