private void Run()
{
List <(double, double)> spoints = Measurements.Select(_ => ((double)(_.Key - odt).TotalSeconds, _.Value)).ToList();
var outgps = gpmm.AddTrainingPoints(spoints.Select(_ => _.Item1).ToArray(), spoints.Select(_ => _.Item2).ToArray());
var xx = outgps.Select(_ => _.mu
.Zip(_.sd95, (a, b) => new { Mu = a, Sigma = b })
.Zip(_.X, (c, d) =>
new Estimate(odt + TimeSpan.FromSeconds(d), c.Mu, c.Sigma)));
Estimates.Clear();
foreach (var __ in xx)
{
foreach (var ___ in __)
{
Estimates.Add(___);
}
}
}
public void Run()
{
var filter = new UKF(1, 1);
n = 1;
q = 0.05;
r = 0.3;
N = 100;
Q = Matrix.Build.Diagonal(n, n, q * q); //covariance of process
NotifyChanged("Q");
R = Matrix.Build.Dense(1, 1, r * r); //covariance of measurement
f = new FEquation(); //nonlinear state equations
h = new HEquation(); //measurement equation
x = q * Matrix.Build.Random(1, 1); //s + q * Matrix.Build.Random(1, 1); //initial state with noise
P = Matrix.Build.Diagonal(n, n, 1); //initial state covariance
var xV = Matrix.Build.Dense(n, N, 0); //Estimate
var zV = Matrix.Build.Dense(1, N, 0); //measurement
for (int k = 1; k < N; k++)
{
Matrix <double> z = ProcessBuilder.SineWave(k, r);
//measurments
Matrix <double>[] x_and_P = filter.Update(f, x, P, h, z, Q, R); //ukf
x = x_and_P[0];
P = x_and_P[1];
Measurements.Add(new Measurement()
{
Value = z[0, 0], Time = TimeSpan.FromSeconds(k)
});
Estimates.Add(new Measurement()
{
Value = x_and_P[0][0, 0], Time = TimeSpan.FromSeconds(k), Variance = x_and_P[1][0, 0]
});
}
}
public void Sample()
{
Estimates.Clear();
gpw.Sample().ForEach(_ => Estimates.Add(_));
}
