public TestSimpleIdentification()
{
TestName = "Простая модель с идентификацией";
TestFileName = "TestSimpleIdentification";
Vector <double> mW = Exts.Vector(0, 0); Matrix <double> dW = Exts.Diag(0, 1.0);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(1.0);
Vector <double> mEta = Exts.Vector(0, 0.0); Matrix <double> dEta = Exts.Diag(0.27, 0);
Func <int, Vector <double>, Vector <double> > phi1 = (s, x) => Exts.Vector(x[0], x[0] * x[1]);
Func <int, Vector <double>, Matrix <double> > phi2 = (s, x) => Exts.Diag(0.0, 1.0);
Func <int, Vector <double>, Vector <double> > psi1 = (s, x) => Exts.Vector(x[1]);
Func <int, Vector <double>, Matrix <double> > psi2 = (s, x) => Exts.Matrix(0.1);
RandomVector <Normal> NormalW = new RandomVector <Normal>(mW, dW);
RandomVector <Normal> NormalNu = new RandomVector <Normal>(mNu, dNu);
ContinuousUniform UniformW = new ContinuousUniform(-0.9, 0.9);
Phi1 = phi1;
Phi2 = phi2;
Psi1 = psi1;
Psi2 = psi2;
Xi = (s, x) => phi1(s, x) + phi2(s, x) * mW;
Zeta = (s, x, y, k) => y - psi1(s, x) - psi2(s, x) * mNu;
W = (s) => NormalW.Sample();
Nu = (s) => NormalNu.Sample();
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(UniformW.Sample(), 0.0);
X0Hat = mEta;
DX0Hat = dEta;
}
public TestInverseProportionBadScalar(double bound, double _dw, double _dnu)
{
TestName = "Обратнопропорциональная зависимость (неуст)";
TestFileName = "InverseProportionBad";
Vector <double> mW = Exts.Vector(0); Matrix <double> dW = Exts.Diag(_dw);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(_dnu);
Vector <double> mEta = Exts.Vector(3.01); Matrix <double> dEta = Exts.Diag(1.0 + 1e-6); // FOR AIT (small values are for regularization)
//Vector<double> mEta = Exts.Vector(1.0); Matrix<double> dEta = Exts.Diag(5.0 * 1e5); // FOR IEOPR (no transit)
Func <int, Vector <double>, Vector <double> > phi = (s, x) =>
{
var res = Math.Min(bound, 1.0 / Math.Pow(Math.Abs(x[0]), 2.0));
if (double.IsNaN(res) || double.IsInfinity(res))
{
return(Exts.Vector(bound));
}
else
{
return(Exts.Vector(res));
}
};
Func <int, Vector <double>, Vector <double> > psi = (s, x) => Exts.Vector(x[0]);
//Phi1_latex = new string[] { @"min(" + bound.ToString() + @",\frac{1}{x_t^2})" };
//Psi1_latex = new string[] { @"x_t" };
//P_W = @"\mathcal{N}\left(" + mW.ToLatex() + ", " + mW.ToLatex() + @"\right)";
//P_Nu = @"\mathcal{N}\left(" + mNu.ToLatex() + ", " + dNu.ToLatex() + @"\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
Normal[] NormalW = new Normal[1] {
new Normal(mW[0], Math.Sqrt(dW[0, 0]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[1] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0]))
};
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi;
Psi1 = psi;
Xi = (s, x) => phi(s, x) + mW;
Zeta = (s, x, y, k) => y - psi(s, x) - mNu;
W = (s) => Exts.Vector(NormalW[0].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
public TestLogisticModelZeroScalar(double bound, double _dw, double _dnu)
{
TestName = "Логистическая модель с возвратом";
TestFileName = "LogisticModelZero";
Vector <double> mW = Exts.Vector(0); Matrix <double> dW = Exts.Diag(_dw);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(_dnu);
Vector <double> mEta = Exts.Vector(0.1); Matrix <double> dEta = Exts.Diag(0.01); // small values are for regularization
Func <int, Vector <double>, Vector <double> > phi = (s, x) =>
{
if (Math.Abs(x[0] * (1 - x[0])) < bound)
{
return(Exts.Vector(x[0] * (1 - x[0])));
}
else
{
return(Exts.Vector(0));
}
};
Func <int, Vector <double>, Vector <double> > psi = (s, x) => Exts.Vector(x[0]);
Func <int, Vector <double>, Matrix <double> > psi_test = (s, x) => Matrix <double> .Build.Dense(1, 1, 1.0);
//Phi1_latex = new string[] { @"x(1-x) if abs(x(1-x)) < " + bound.ToString() + "; 0 else" };
//Psi1_latex = new string[] { @"x_t" };
//P_W = @"\mathcal{N}\left(" + mW.ToLatex() + ", " + dW.ToLatex() + @"\right)";
//P_Nu = @"\mathcal{N}\left(" + mNu.ToLatex() + ", " + dNu.ToLatex() + @"\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
Normal[] NormalW = new Normal[1] {
new Normal(mW[0], Math.Sqrt(dW[0, 0]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[1] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0]))
};
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi;
Psi1 = psi;
//Psi2 = psi_test;
Xi = (s, x) => phi(s, x) + mW;
Zeta = (s, x, y, k) => y - psi(s, x) - mNu;
W = (s) => Exts.Vector(NormalW[0].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
public TestCubicSensorScalar(double _dw, double _dnu)
{
TestName = "Кубический сенсор";
TestFileName = "CubicSensor";
Vector <double> mW = Exts.Vector(0); Matrix <double> dW = Exts.Diag(_dw);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(_dnu);
Vector <double> mEta = Exts.Vector(0.1); Matrix <double> dEta = Exts.Diag(1.16);
Func <int, Vector <double>, Vector <double> > phi = (s, x) => Exts.Vector(x[0] / (1 + x[0] * x[0]));
Func <int, Vector <double>, Vector <double> > psi = (s, x) => Exts.Vector(Math.Pow(x[0], 3) + Math.Pow(x[0], 1));
Func <int, Vector <double>, Matrix <double> > dpsi = (s, x) => Exts.Matrix(3.0 * Math.Pow(x[0], 2) + 1.0);
//Phi1_latex = new string[] { @"\frac{x_t}{1 + x_t^2}"};
//Psi1_latex = new string[] { @"x_t^3+x_t"};
//P_W = @"\mathcal{N}\left(" + mW.ToLatex() + ", " + mW.ToLatex() + @"\right)";
//P_Nu = @"\mathcal{N}\left(" + mNu.ToLatex() + ", " + dNu.ToLatex() + @"\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
Normal[] NormalW = new Normal[1] {
new Normal(mW[0], Math.Sqrt(dW[0, 0]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[1] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0]))
};
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi;
Psi1 = psi;
dPhi = (s, x) => Exts.Diag(0.9);
dPsi = (s, x) => Exts.Diag(1.0);
Xi = (s, x) => phi(s, x) + mW;
Zeta = (s, x, y, k) => y - psi(s, x) - mNu;
//Zeta = (s, x, y, k) => k * dpsi(s,x).Transpose() * (dpsi(s,x) * k * dpsi(s,x).Transpose() + dNu ).PseudoInverse() * (y - psi(s, x) - mNu);
W = (s) => Exts.Vector(NormalW[0].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
public TestCubicSensor(double _dw, double _dnu)
{
TestName = "Кубический сенсор";
TestFileName = "CubicSensor";
Vector <double> mW = Exts.Vector(0, 0); Matrix <double> dW = Exts.Diag(_dw, _dw);
Vector <double> mNu = Exts.Vector(0, 0); Matrix <double> dNu = Exts.Diag(_dnu, _dnu);
//Vector<double> mEta = Exts.Vector(100, 100); Matrix<double> dEta = Exts.Diag(100, 100);
Vector <double> mEta = Exts.Vector(0, 0); Matrix <double> dEta = Exts.Diag(1, 1);
Func <int, Vector <double>, Vector <double> > phi = (s, x) => Exts.Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1]));
Func <int, Vector <double>, Vector <double> > psi = (s, x) => Exts.Vector(Math.Pow(x[0], 3) + Math.Pow(x[0], 1), Math.Pow(x[1], 3) + Math.Pow(x[1], 1));
Func <int, Vector <double>, Matrix <double> > psi_test = (s, x) => Matrix <double> .Build.Dense(1, 1, 1.0);
Psi2 = psi_test;
//Phi1_latex = new string[] { @"\frac{x_0}{1 + x_0^2}", @"\frac{x_1}{1 + x_1^2}" };
//Psi1_latex = new string[] { @"x_0^3+x_0", @"x_1^3+x_1" };
//P_W = @"\mathcal{N}\left(\mathbf{0}, \mathbf{E}\right)";
//P_Nu = @"\mathcal{N}\left(\mathbf{0}, \mathbf{E}\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
Normal[] NormalW = new Normal[2] {
new Normal(mW[0], Math.Sqrt(dW[0, 0])), new Normal(mW[1], Math.Sqrt(dW[1, 1]))
};
Normal[] NormalNu = new Normal[2] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0])), new Normal(mNu[1], Math.Sqrt(dNu[1, 1]))
};;
Normal[] NormalEta = new Normal[2] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0])), new Normal(mEta[1], Math.Sqrt(dEta[1, 1]))
};;
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi;
Psi1 = psi;
Xi = (s, x) => phi(s, x) + mW;
Zeta = (s, x, y, k) => y - psi(s, x) - mNu;
W = (s) => Exts.Vector(NormalW[0].Sample(), NormalW[1].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample(), NormalNu[1].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample(), NormalEta[1].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
public TestLogisticModelUniformNoiseScalar()
{
TestName = "Логистическая модель с равномерным шумом";
TestFileName = "LogisticModelUniform";
Vector <double> mW = Exts.Vector(1e-5); Matrix <double> dW = Exts.Diag(1.0 / 3.0);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(1);
Vector <double> mEta = Exts.Vector(0.5); Matrix <double> dEta = Exts.Diag(0.01); // small values are for regularization
Func <int, Vector <double>, Vector <double> > phi1 = (s, x) => Exts.Vector(3.0 * x[0] * (1 - x[0]));
Func <int, Vector <double>, Matrix <double> > phi2 = (s, x) => Exts.Diag(x[0] * (1 - x[0]));
Func <int, Vector <double>, Vector <double> > psi = (s, x) => Exts.Vector(x[0]);
//Phi1_latex = new string[] { @"???" };
//Psi1_latex = new string[] { @"x_t" };
//P_W = @"\mathcal{R}\left(0,1\right)";
//P_Nu = @"\mathcal{N}\left(" + mNu.ToLatex() + ", " + dNu.ToLatex() + @"\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
ContinuousUniform[] UniformW = new ContinuousUniform[1] {
new ContinuousUniform(-1 + 1e-5, 1 + 1e-5)
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[1] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0]))
};
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi1;
Phi2 = phi2;
Psi1 = psi;
Xi = (s, x) => phi1(s, x) + phi2(s, x) * mW;
Zeta = (s, x, y, k) => y - psi(s, x) - mNu;
W = (s) => Exts.Vector(UniformW[0].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
public TestLogisticModelScalar(double bound, double _dw, double _dnu)
{
TestName = "Логистическая модель";
TestFileName = "LogisticModel";
Vector <double> mW = Exts.Vector(0); Matrix <double> dW = Exts.Diag(_dw);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(_dnu);
Vector <double> mEta = Exts.Vector(0.1); Matrix <double> dEta = Exts.Diag(0.01); // small values are for regularization
Func <int, Vector <double>, Vector <double> > phi = (s, x) => Exts.Vector(Math.Max(-bound, Math.Min(bound, x[0] * (1 - x[0]))));
Func <int, Vector <double>, Vector <double> > psi = (s, x) => Exts.Vector(x[0]);
//Phi1_latex = new string[] { @"max(-" + bound.ToString() + @", min(" + bound.ToString() + @",x(1-x)))" };
//Psi1_latex = new string[] { @"x_t" };
//P_W = @"\mathcal{N}\left(" + mW.ToLatex() + ", " + dW.ToLatex() + @"\right)";
//P_Nu = @"\mathcal{N}\left(" + mNu.ToLatex() + ", " + dNu.ToLatex() + @"\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
Normal[] NormalW = new Normal[1] {
new Normal(mW[0], Math.Sqrt(dW[0, 0]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[1] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0]))
};
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi;
Psi1 = psi;
Xi = (s, x) => phi(s, x) + mW;
Zeta = (s, x, y, k) => y - psi(s, x) - mNu;
W = (s) => Exts.Vector(NormalW[0].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
static void Run(Options o, string[] args)
{
if (string.IsNullOrWhiteSpace(o.ScriptsFolder))
{
o.ScriptsFolder = "..\\..\\..\\OutputScripts\\";
}
Directory.CreateDirectory(o.OutputFolder);
using (System.IO.StreamWriter outputfile = new System.IO.StreamWriter(Path.Combine(o.OutputFolder, "parameters.txt"), true))
{
outputfile.WriteLine($"{DateTime.Now}\t{string.Join(" ", args)}");
outputfile.Close();
}
// original continuous model
// x_t = x_0 + \int_0^t Phi_1(x_t) dt + \int_0^t Phi_2(x_t) dW_t
// discrete model:
// x_{t+1} = Phi_1(x_t) + Phi_2(x_t) W_t
//observations
// y_t = Psi_1(x_t) + Psi_2(x_t) Nu_t
#region 3d model Ienkaran Arasaratnam, Simon Haykin, and Tom R. Hurd
//mpdel params
//double sigma1 = Math.Sqrt(0.2);
//double sigma2 = 7.0 * 1e-3;
//double sigma_r = 50;
//double sigma_th = 0.1;
//double sigma_ph = 0.1;
// starting point
//Vector<double> mEta = Exts.Vector(1000, 0, 2650, 150, 200, 0, 1.0);
//Matrix<double> dEta = Exts.Diag(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0);
//RandomVector<Normal> NormalEta = new RandomVector<Normal>(mEta, dEta);
//Func<Vector<double>> X0;
////X0 = () => NormalEta.Sample();
//X0 = () => mEta;
// dynamics
//Func<Vector<double>, Vector<double>> Phi1 = (x) => Exts.Vector(x[1], -x[6] * x[3], x[3], x[6] * x[1], x[5], 0, 0);
//Func<Matrix<double>> Phi2 = () => Exts.Diag(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0);
//Vector<double> mW = Exts.Vector(0, 0, 0, 0, 0, 0, 0);
//Matrix<double> dW = Exts.Diag(1e1, Math.Pow(sigma1, 2), 1e1, Math.Pow(sigma1, 2), 1e1, Math.Pow(sigma1, 2), Math.Pow(sigma2, 2));
//RandomVector<Normal> NormalW = new RandomVector<Normal>(mW, dW);
//Func<Vector<double>> W;
//W = () => NormalW.Sample();
// observations
//Func<Vector<double>, Vector<double>> Psi1 = (x) => Utils.cart2sphere(Exts.Vector(x[0], x[2], x[4]));
//Func<Matrix<double>> Psi2 = () => Exts.Diag(1.0, 1.0, 1.0);
//Vector<double> mNu = Exts.Vector(0, 0, 0);
//Matrix<double> dNu = Exts.Diag(Math.Pow(sigma_r, 2), Math.Pow(sigma_th, 2), Math.Pow(sigma_ph, 2));
////Vector<double> X_R2 = Exts.Vector(0, 0);
////Vector<double> mNu = Exts.Vector(0, 0, 0, 0);
////Matrix<double> dNu = Exts.Diag(Math.Pow(0.1 * Math.PI / 180, 2), Math.Pow(50, 2), Math.Pow(0.1 * Math.PI / 180, 2), Math.Pow(50, 2));
////Func<double, Vector<double>, Vector<double>> Psi1 = (s, x) => Exts.Stack(Utils.cart2pol(Exts.Vector(x[0], x[1]) - X_R1), Utils.cart2pol(Exts.Vector(x[0], x[1]) - X_R2));
////Func<double, Vector<double>, Matrix<double>> Psi2 = (s, x) => Exts.Diag(1.0, 1.0, 1.0, 1.0);
//RandomVector<Normal> NormalNu = new RandomVector<Normal>(mNu, dNu);
//Func<Vector<double>> Nu;
//Nu = () => NormalNu.Sample();
//double h_state = 0.01;
//double h_obs = 0.01;
//double T = 1.0 + h_state / 2;
#endregion
#region 2d model with acceleration
// model params
double Alpha_n = o.alpha; // 0.05;
double Beta_n = o.beta; //1.0;
double Gamma_n = o.gamma; //0.5;
// starting point
Vector <double> mEta = Exts.Vector(0, 25000, 400, 10 * Math.PI / 180, Gamma_n / Alpha_n);
Matrix <double> dEta = Exts.Diag(Math.Pow(o.DX0, 2), Math.Pow(o.DX0, 2), Math.Pow(115, 2), Math.Pow(15 * Math.PI / 180, 2), Math.Pow(Beta_n, 2) / 2 / Alpha_n);
RandomVector <Normal> NormalEta = new RandomVector <Normal>(mEta, dEta);
ContinuousUniform UniformEtaV = new ContinuousUniform(200, 600);
Func <Vector <double> > X0;
X0 = () =>
{
var x = NormalEta.Sample();
x[2] = UniformEtaV.Sample();
return(x);
};
// dynamics
Func <Vector <double>, Vector <double> > Phi1 = (x) => Exts.Vector(x[2] * Math.Cos(x[3]), x[2] * Math.Sin(x[3]), 0, x[4] / x[2], -Alpha_n * x[4] + Gamma_n);
Func <Matrix <double> > Phi2 = () => Exts.Diag(0, 0, 0, 0, 1.0);
Vector <double> mW = Exts.Vector(0, 0, 0, 0, 0);
Matrix <double> dW = Exts.Diag(0, 0, 0, 0, Math.Pow(Beta_n, 2));
Normal NormalW = new Normal(mW[4], Math.Sqrt(dW[4, 4]));
Func <Vector <double> > W;
W = () => Exts.Vector(0, 0, 0, 0, NormalW.Sample());
// observations
Vector <double> X_R1 = Exts.Vector(-10000, 10000); // first radar location
Vector <double> X_R2 = Exts.Vector(0, 0); // second radar location
Func <Vector <double>, Vector <double> > Psi1 = (x) => Exts.Stack(Utils.cart2pol(Exts.Vector(x[0], x[1]) - X_R1), Utils.cart2pol(Exts.Vector(x[0], x[1]) - X_R2));
Func <Matrix <double> > Psi2 = () => Exts.Diag(1.0, 1.0, 1.0, 1.0);
Vector <double> mNu = Exts.Vector(0, 0, 0, 0);
Matrix <double> dNu = Exts.Diag(Math.Pow(0.1 * Math.PI / 180, 2), Math.Pow(50, 2), Math.Pow(0.1 * Math.PI / 180, 2), Math.Pow(50, 2));
RandomVector <Normal> NormalNu = new RandomVector <Normal>(mNu, dNu);
Func <Vector <double> > Nu;
Nu = () => NormalNu.Sample();
//discretization
double h_state = o.h_state; //0.01;
double h_obs = o.h_obs; //1.0;
double T = o.T + h_state / 2;
Func <int, Vector <double>, Vector <double> > Phi1_discr = (i, x) =>
{
Vector <double> xx = x;
for (double s = h_state; s < h_obs + h_state / 2; s += h_state)
{
xx += h_state * Phi1(xx);
}
return(xx);
};
Func <int, Vector <double>, Matrix <double> > Phi2_discr = (i, x) => Math.Sqrt(h_obs) * Phi2();
Func <int, Vector <double>, Vector <double> > Psi1_discr = (i, x) => Psi1(x);
Func <int, Vector <double>, Matrix <double> > Psi2_discr = (i, x) => Psi2();
// derivatives for the Extended Kalman Filter
Func <Vector <double>, Matrix <double> > dPhi = (x) => Matrix <double> .Build.Dense(5, 5, new double[5 * 5] {
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
Math.Cos(x[3]), Math.Sin(x[3]), 0, -x[4] / Math.Pow(x[2], 2), 0,
-x[2] * Math.Sin(x[3]), x[2] * Math.Cos(x[3]), 0, 0, 0,
0, 0, 0, 1.0 / x[2], -Alpha_n
}); // Column-major order
Func <Vector <double>, Matrix <double> > dPsi = (x) =>
{
var x1 = x - X_R1.Stack(Exts.Vector(0, 0, 0));
var r1r1 = x1[0] * x1[0] + x1[1] * x1[1];
var r1 = Math.Sqrt(r1r1);
var x2 = x - X_R2.Stack(Exts.Vector(0, 0, 0));
var r2r2 = x2[0] * x2[0] + x2[1] * x2[1];
var r2 = Math.Sqrt(r2r2);
return(Matrix <double> .Build.Dense(4, 5, new double[5 * 4] {
-x1[1] / r1r1, x1[0] / r1, -x2[1] / r2r2, x2[0] / r2,
x1[0] / r1r1, x1[1] / r1, x2[0] / r2r2, x2[1] / r2,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
})); // Column-major order
};
Func <int, Vector <double>, Matrix <double>, (Vector <double>, Matrix <double>)> Ricatti = (i, x, P) =>
{
Vector <double> xx = x;
Matrix <double> PP = P;
for (double s = h_state; s < h_obs + h_state / 2; s += h_state)
{
PP += h_state * (dPhi(xx) * PP + PP * dPhi(xx).Transpose() + Phi2() * dW * Phi2().Transpose());
xx += h_state * (Phi1(xx) + Phi2() * mW);
}
return(xx, PP);
};
//trivial estimate
Func <int, Vector <double>, Vector <double>, Matrix <double>, (Vector <double>, Matrix <double>)> DummyEstimate = (i, y, x, P) =>
{
Vector <double> xx = x;
Matrix <double> PP = P;
for (double s = h_state; s < h_obs + h_state / 2; s += h_state)
{
PP += h_state * (dPhi(xx) * PP + PP * dPhi(xx).Transpose() + Phi2() * dW * Phi2().Transpose());
xx += h_state * (Phi1(xx) + Phi2() * mW);
}
return((0.5 * (Utils.pol2cart(Exts.Vector(y[0], y[1])) + X_R1 + Utils.pol2cart(Exts.Vector(y[2], y[3])) + X_R2)).Stack(Exts.Vector(xx[2], xx[3], xx[4])), PP);
};
#endregion
int N = (int)(T / h_obs);
Func <DiscreteVectorModel> ModelGenerator = () =>
{
DiscreteVectorModel model = null;
int n = 0;
double h_tolerance = h_state / 2.0;
double t_nextobservation = h_obs;
Vector <double> State = X0();
Vector <double> Obs;
model = new DiscreteVectorModel(Phi1_discr, Phi2_discr, Psi1_discr, Psi2_discr, (i) => W(), (i) => Nu(), X0(), true);
//for (double s = 0; s < T; s += h_obs)
//{
// model.Step();
//}
for (double s = h_state; s < T; s += h_state)
{
if (s > 0)
{
State = State + h_state * Phi1(State) + Math.Sqrt(h_state) * Phi2() * W();
}
if (Math.Abs(s - t_nextobservation) < h_tolerance)
{
Obs = Psi1(State) + Psi2() * Nu();
t_nextobservation += h_obs;
n++;
model.Trajectory.Add(n, new Vector <double>[] { State, Obs });
}
}
return(model);
};
// filter params file names
string CMNFFileName = Path.Combine(o.OutputFolder, "cmnf.params");
if (!string.IsNullOrWhiteSpace(o.CMNFFileName))
{
CMNFFileName = o.CMNFFileName;
}
string BCMNFFileName = Path.Combine(o.OutputFolder, "bcmnf.params");
if (!string.IsNullOrWhiteSpace(o.BCMNFFileName))
{
BCMNFFileName = o.BCMNFFileName;
}
string UKFFileName = Path.Combine(o.OutputFolder, "ukf.params");
if (!string.IsNullOrWhiteSpace(o.UKFFileName))
{
UKFFileName = o.UKFFileName;
}
string UKFOptStepwiseNMFileName = Path.Combine(o.OutputFolder, "ukfoptstepwiseNM.params");
if (!string.IsNullOrWhiteSpace(o.UKFStepwiseNelderMeadFileName))
{
UKFOptStepwiseNMFileName = o.UKFStepwiseNelderMeadFileName;
}
string UKFOptIntegralNMFileName = Path.Combine(o.OutputFolder, "ukfoptintegralNM.params");
if (!string.IsNullOrWhiteSpace(o.UKFIntegralNelderMeadFileName))
{
UKFOptIntegralNMFileName = o.UKFIntegralNelderMeadFileName;
}
string UKFOptStepwiseRandFileName = Path.Combine(o.OutputFolder, "ukfoptstepwiserand.params");
if (!string.IsNullOrWhiteSpace(o.UKFStepwiseRandomShootFileName))
{
UKFOptStepwiseRandFileName = o.UKFStepwiseRandomShootFileName;
}
string UKFOptIntegralRandFileName = Path.Combine(o.OutputFolder, "ukfoptintegralrand.params");
if (!string.IsNullOrWhiteSpace(o.UKFIntegralRandomShootFileName))
{
UKFOptIntegralRandFileName = o.UKFIntegralRandomShootFileName;
}
// filters
List <(FilterType, string)> filters = new List <(FilterType, string)>();
if (o.CMNF)
{
filters.Add((FilterType.CMNF, CMNFFileName));
}
if (o.BCMNF)
{
filters.Add((FilterType.BCMNF, BCMNFFileName));
}
if (o.MCMNF)
{
filters.Add((FilterType.MCMNF, string.Empty));
}
if (o.UKF)
{
filters.Add((FilterType.UKFNoOptimization, UKFFileName));
}
if (o.UKFStepwiseNelderMead)
{
filters.Add((FilterType.UKFStepwise, UKFOptStepwiseNMFileName));
}
if (o.UKFIntegralNelderMead)
{
filters.Add((FilterType.UKFIntegral, UKFOptIntegralNMFileName));
}
if (o.UKFStepwiseRandomShoot)
{
filters.Add((FilterType.UKFStepwiseRandomShoot, UKFOptStepwiseRandFileName));
}
if (o.UKFIntegralRandomShoot)
{
filters.Add((FilterType.UKFIntegralRandomShoot, UKFOptIntegralRandFileName));
}
if (o.EKF)
{
filters.Add((FilterType.EKF, string.Empty));
}
if (o.Dummy)
{
filters.Add((FilterType.Dummy, string.Empty));
}
// test environment
TestEnvironmentVector testEnv = new TestEnvironmentVector()
{
TestName = "Target tracking",
TestFileName = "TargetTracking",
Phi1 = Phi1_discr,
Phi2 = Phi2_discr,
Psi1 = Psi1_discr,
Psi2 = Psi2_discr,
dPhi = (i, x) => dPhi(x),
dPsi = (i, x) => dPsi(x),
Xi = (i, x) => Phi1_discr(i, x) + Phi2_discr(i, x) * mW,
//Zeta = (i, x, y, k) => (y - Psi1_discr(i, x) - Psi2_discr(i, x) * mNu).Stack(Utils.pol2cart(Exts.Vector(y[0], y[1]))+X_R1).Stack(Utils.pol2cart(Exts.Vector(y[2], y[3]))+X_R2),
Zeta = (i, x, y, k) => (y - Psi1_discr(i, x) - Psi2_discr(i, x) * mNu),
Alpha = (i, x) => Phi1_discr(i, x) + Phi2_discr(i, x) * mW,
Gamma = (i, x, y) => (y).Stack(Utils.pol2cart(Exts.Vector(y[0], y[1])) + X_R1).Stack(Utils.pol2cart(Exts.Vector(y[2], y[3])) + X_R2),
//Gamma = (i, x, y) => y - Psi1_discr(i, x) - Psi2_discr(i, x) * mNu,
nMCMNF = o.MCMNFTrainCount,
W = (i) => W(),
Nu = (i) => Nu(),
DW = dW,
DNu = dNu,
X0 = () => X0(),
X0Hat = mEta,
DX0Hat = dEta,
Predict = Ricatti,
DummyEstimate = DummyEstimate,
ModelGenerator = ModelGenerator
};
if (o.Bulk)
{
testEnv.GenerateBundleSamples(o.T, o.TrainCount, o.OutputFolder);
}
else
{
testEnv.Initialize(o.T, o.TrainCount, o.OutputFolder, filters, o.Save, o.Load);
if (o.Sift)
{
testEnv.Sifter = (x) => Math.Sqrt(x[0] * x[0] + x[1] * x[1]) > o.SiftBound;
}
if (o.Aggregate)
{
testEnv.Aggregate(o.OutputFolder, o.OutputFolder, !o.NoBin, !o.NoText);
}
if (!o.Skip)
{
if (o.SamplesCount == 0)
{
testEnv.GenerateBundles(o.BundleCount, o.TestCount, o.OutputFolder, o.Parallel, o.ParallelismDegree, !o.NoBin, !o.NoText);
if (!o.NoPython)
{
testEnv.RunScript(Path.Combine(o.ScriptsFolder, "estimate_statistics.py"), o.OutputFolder);
}
}
else
{
if (o.SamplesCount == 1)
{
testEnv.GenerateOne(o.OutputFolder);
if (!o.NoPython)
{
testEnv.RunScript(Path.Combine(o.ScriptsFolder, "estimate_sample.py"), o.OutputFolder);
testEnv.RunScript(Path.Combine(o.ScriptsFolder, "trajectory.py"), o.OutputFolder);
}
}
else
{
for (int i = 0; i < o.SamplesCount; i++)
{
testEnv.GenerateOne(o.OutputFolder, i);
}
}
}
}
}
}
public TestSampledRegression(double _dnu)
{
{
TestName = "Модель семплированной регрессии";
TestFileName = "SampledRegression";
Vector <double> a = Exts.Vector(0.3, 0.4, 0.7);
Vector <double> b = Exts.Vector(1.4, 3.0, 3.0);
Vector <double> c = Exts.Vector(0.9, 1.5, 2.5);
Vector <double> d = Exts.Vector(0.33, 0.37, 0.3);
Vector <double> m = Exts.Vector(b[0] / (1 - a[0]), b[1] / (1 - a[1]), b[2] / (1 - a[2]));
Vector <double> S = Exts.Vector(c[0] / Math.Sqrt(1 - a[0] * a[0]), c[1] / Math.Sqrt(1 - a[1] * a[1]), c[2] / Math.Sqrt(1 - a[2] * a[2]));
Func <double, int> I = x =>
{
if (x < 3)
{
return(0);
}
else if (x < 7)
{
return(1);
}
else
{
return(2);
}
};
Vector <double> mW = Exts.Vector(0); Matrix <double> dW = Exts.Diag(1.0);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(_dnu);
Vector <double> mEta = Exts.Vector(0); Matrix <double> dEta = Exts.Diag(1.0);
Func <int, Vector <double>, Vector <double> > phi1 = (s, x) => Exts.Vector(a[I(x[0])] * x[0] + b[I(x[0])]);
Func <int, Vector <double>, Matrix <double> > phi2 = (s, x) => Exts.Matrix(c[I(x[0])]);
Func <int, Vector <double>, Vector <double> > psi = (s, x) => Exts.Vector(x[0]);
//Phi1_latex = new string[] { @"a^T e(x_t) x_t + b^T e(x_t)" };
//Phi2_latex = new string[][] { new string[] { @"c^T e(x_t)" } };
//Psi1_latex = new string[] { @"x_t" };
//P_W = @"\mathcal{N}\left(" + mW.ToLatex() + ", " + dW.ToLatex() + @"\right)";
//P_Nu = @"\mathcal{N}\left(" + mNu.ToLatex() + ", " + dNu.ToLatex() + @"\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
Normal[] NormalW = new Normal[1] {
new Normal(mW[0], Math.Sqrt(dW[0, 0]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[1] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0]))
};
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi1;
Phi2 = phi2;
Psi1 = psi;
//Xi = (s, x) => phi1(s, x) + phi2(s, x) * mW;
Xi = (s, x) =>
{
double num = 0;
double den = 0;
for (int i = 0; i < a.Count; i++)
{
num += d[i] * Normal.PDF(m[i], S[i], x[0]) * (a[i] * x[0] + b[i]);
den += d[i] * Normal.PDF(m[i], S[i], x[0]);
}
return(Exts.Vector(num / den));
};
Zeta = (s, x, y, k) => y - psi(s, x) - mNu;
W = (s) => Exts.Vector(NormalW[0].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
}
public HopefullyTheLastTestSwitchingObservationsIdentification(double _dnu)
{
{
TestName = "Надеюсь, последняя модель с переключающимися каналами наблюдений и идентификацией";
TestFileName = "HopefullyTheLastSwitchingObservationsIdentification";
Vector <double> sig = Exts.Vector(1.0, 4.0, 10.0);
double l1 = -0.6745;
double l2 = 0.6745;
Func <double, int> I = x =>
{
if (x < l1)
{
return(0);
}
else if (x < l2)
{
return(1);
}
else
{
return(2);
}
};
Func <double, double> F = x =>
{
return(0.4 - x * Math.Exp(-0.3 * x * x));
};
//Vector<double> f = Exts.Vector(Normal.CDF(m, S, l1), Normal.CDF(m, S, l2) - Normal.CDF(m, S, l1), 1.0 - Normal.CDF(m, S, l2));
Vector <double> f = Exts.Vector(0.25, 0.5, 0.25);
Vector <double> mW = Exts.Vector(0, 0); Matrix <double> dW = Exts.Diag(1e-5, 1.0);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(1e-5);
Vector <double> mEta = Exts.Vector(0, 0.0); Matrix <double> dEta = Exts.Diag(0.27, 1e-5);
Func <int, Vector <double>, Vector <double> > phi1 = (s, x) => Exts.Vector(x[0], x[0] * F(x[1]));
Func <int, Vector <double>, Matrix <double> > phi2 = (s, x) => Exts.Diag(1.0, sig[I(x[1])]);
Func <int, Vector <double>, Vector <double> > psi1 = (s, x) => Exts.Vector(x[1]);
Func <int, Vector <double>, Matrix <double> > psi2 = (s, x) => Exts.Matrix(1.0);
Normal[] NormalW = new Normal[2] {
new Normal(mW[0], Math.Sqrt(dW[0, 0])), new Normal(mW[1], Math.Sqrt(dW[1, 1]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
ContinuousUniform UniformW = new ContinuousUniform(-0.9, 0.9);
Phi1 = phi1;
Phi2 = phi2;
Psi1 = psi1;
Psi2 = psi2;
Xi = (s, x) => phi1(s, x) + phi2(s, x) * mW;
Zeta = (s, x, y, k) => y - psi1(s, x) - psi2(s, x) * mNu;
W = (s) => Exts.Vector(NormalW[0].Sample(), NormalW[1].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(UniformW.Sample(), 0.0);
X0Hat = mEta;
DX0Hat = dEta;
}
}
public TestSwitchingObservationsIdentification(double _dnu)
{
{
TestName = "Модель с переключающимися каналами наблюдений и идентификацией";
TestFileName = "SwitchingObservationsIdentification";
double a = 0.8;
double b = 0.2;
double c = 6.0;
//Vector<double> d = Exts.Vector(10.0, 1.0, 1.0);
//Vector<double> sig = Exts.Vector(1.0, 1.0, 10.0);
Vector <double> d = Exts.Vector(4.0, 1.0, 0.5);
Vector <double> sig = Exts.Vector(1.0, 1.0, 3.0);
double m = b / (1 - a);
double S = Math.Sqrt(c * c / (1 - a * a));
double l1 = -0.6745;
double l2 = 0.6745;
Func <double, int> I = x =>
{
if (x < l1)
{
return(0);
}
else if (x < l2)
{
return(1);
}
else
{
return(2);
}
};
//Vector<double> f = Exts.Vector(Normal.CDF(m, S, l1), Normal.CDF(m, S, l2) - Normal.CDF(m, S, l1), 1.0 - Normal.CDF(m, S, l2));
Vector <double> f = Exts.Vector(0.25, 0.5, 0.25);
Vector <double> mW = Exts.Vector(0, 0, a); Matrix <double> dW = Exts.Diag(1.0, 1.0, 0.04 / 3.0);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(_dnu);
Vector <double> mEta = Exts.Vector(0, 0.0, a); Matrix <double> dEta = Exts.Diag(1.0, 1.0, 0.04 / 3.0);
Func <int, Vector <double>, Vector <double> > phi1 = (s, x) => Exts.Vector(x[2] * x[0] + b, 0, 0);
Func <int, Vector <double>, Matrix <double> > phi2 = (s, x) => Exts.Diag(c, 1.0, 1.0);
Func <int, Vector <double>, Vector <double> > psi1 = (s, x) => Exts.Vector(d[I(x[1])] * x[0]);
Func <int, Vector <double>, Matrix <double> > psi2 = (s, x) => Exts.Matrix(sig[I(x[1])]);
Normal[] NormalW = new Normal[2] {
new Normal(mW[0], Math.Sqrt(dW[0, 0])), new Normal(mW[1], Math.Sqrt(dW[1, 1]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[2] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0])), new Normal(mEta[1], Math.Sqrt(dEta[1, 1]))
};
ContinuousUniform UniformW = new ContinuousUniform(a - 0.2, a + 0.2);
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi1;
Phi2 = phi2;
Psi1 = psi1;
Psi2 = psi2;
Xi = (s, x) => phi1(s, x) + phi2(s, x) * mW;
//Zeta = (s, x, y, k) => y - psi1(s, x) - psi2(s, x) * mNu;
Zeta = (s, x, y, k) =>
{
double num = 0;
double den = 0;
for (int i = 0; i < d.Count; i++)
{
double xi = k[0, 0] * d[i] / (d[i] * d[i] * k[0, 0] + sig[i] * sig[i]) * (y[0] - d[i] * x[0]);
num += f[i] * Normal.PDF(d[i] * x[0], Math.Sqrt(d[i] * d[i] * k[0, 0] + sig[i] * sig[i]), y[0]) * xi;
den += f[i] * Normal.PDF(d[i] * x[0], Math.Sqrt(d[i] * d[i] * k[0, 0] + sig[i] * sig[i]), y[0]);
}
return(Exts.Vector(num / den));
};
W = (s) => Exts.Vector(NormalW[0].Sample(), NormalW[1].Sample(), UniformW.Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample(), NormalEta[1].Sample(), UniformW.Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
}
public TestSwitchingObservations(double _dnu)
{
{
TestName = "Модель с переключающимися каналами наблюдений";
TestFileName = "SwitchingObservations";
double a = 0.8;
double b = 0.2;
double c = 6.0;
//Vector<double> d = Exts.Vector(10.0, 1.0, 1.0);
//Vector<double> sig = Exts.Vector(1.0, 1.0, 10.0);
Vector <double> d = Exts.Vector(4.0, 1.0, 0.5);
Vector <double> sig = Exts.Vector(1.0, 1.0, 3.0);
double m = b / (1 - a);
double S = Math.Sqrt(c * c / (1 - a * a));
double l1 = -0.6745;
double l2 = 0.6745;
Func <double, int> I = x =>
{
if (x < l1)
{
return(0);
}
else if (x < l2)
{
return(1);
}
else
{
return(2);
}
};
//Vector<double> f = Exts.Vector(Normal.CDF(m, S, l1), Normal.CDF(m, S, l2) - Normal.CDF(m, S, l1), 1.0 - Normal.CDF(m, S, l2));
Vector <double> f = Exts.Vector(0.25, 0.5, 0.25);
Vector <double> mW = Exts.Vector(0, 0.0); Matrix <double> dW = Exts.Diag(1.0, 1.0);
Vector <double> mNu = Exts.Vector(0); Matrix <double> dNu = Exts.Diag(_dnu);
Vector <double> mEta = Exts.Vector(0, 0.0); Matrix <double> dEta = Exts.Diag(1.0, 1.0); // FOR AIT
//Vector<double> mEta = Exts.Vector(1.0, 0.0); Matrix<double> dEta = Exts.Diag(100.0, 1.0); // FOR IEOPR (no transit)
Func <int, Vector <double>, Vector <double> > phi1 = (s, x) => Exts.Vector(a * x[0] + b, 0);
Func <int, Vector <double>, Matrix <double> > phi2 = (s, x) => Exts.Diag(c, 1.0);
Func <int, Vector <double>, Vector <double> > psi1 = (s, x) => Exts.Vector(d[I(x[1])] * x[0]);
Func <int, Vector <double>, Matrix <double> > psi2 = (s, x) => Exts.Matrix(sig[I(x[1])]);
//Phi1_latex = new string[] { @"a x_t + b", "0" };
//Phi2_latex = new string[][] { new string[] { @"c", "0" }, new string[] { @"0", "1" }};
//Psi1_latex = new string[] { @"d^T * e(x^1_t) * x_t" };
//Psi2_latex = new string[][] { new string[] { @"\sigma^T * e(x^1_t)" } };
//P_W = @"\mathcal{N}\left(" + mW.ToLatex() + ", " + dW.ToLatex() + @"\right)";
//P_Nu = @"\mathcal{N}\left(" + mNu.ToLatex() + ", " + dNu.ToLatex() + @"\right)";
//P_Eta = @"\mathcal{N}\left(" + mEta.ToLatex() + ", " + dEta.ToLatex() + @"\right)";
Normal[] NormalW = new Normal[2] {
new Normal(mW[0], Math.Sqrt(dW[0, 0])), new Normal(mW[1], Math.Sqrt(dW[1, 1]))
};
Normal[] NormalNu = new Normal[1] {
new Normal(mNu[0], Math.Sqrt(dNu[0, 0]))
};
Normal[] NormalEta = new Normal[2] {
new Normal(mEta[0], Math.Sqrt(dEta[0, 0])), new Normal(mEta[1], Math.Sqrt(dEta[1, 1]))
};
//Expression<Func<int, Vector<double>, Vector<double>>> expr = (s, x) => Vector(x[0] / (1 + x[0] * x[0]), x[1] / (1 + x[1] * x[1])); ;
Phi1 = phi1;
Phi2 = phi2;
Psi1 = psi1;
Psi2 = psi2;
Xi = (s, x) => phi1(s, x) + phi2(s, x) * mW;
//Zeta = (s, x, y, k) => y - psi1(s, x) - psi2(s, x) * mNu;
Zeta = (s, x, y, k) =>
{
double num = 0;
double den = 0;
for (int i = 0; i < d.Count; i++)
{
double xi = k[0, 0] * d[i] / (d[i] * d[i] * k[0, 0] + sig[i] * sig[i]) * (y[0] - d[i] * x[0]);
num += f[i] * Normal.PDF(d[i] * x[0], Math.Sqrt(d[i] * d[i] * k[0, 0] + sig[i] * sig[i]), y[0]) * xi;
den += f[i] * Normal.PDF(d[i] * x[0], Math.Sqrt(d[i] * d[i] * k[0, 0] + sig[i] * sig[i]), y[0]);
}
return(Exts.Vector(num / den));
};
W = (s) => Exts.Vector(NormalW[0].Sample(), NormalW[1].Sample());
Nu = (s) => Exts.Vector(NormalNu[0].Sample());
DW = dW;
DNu = dNu;
X0 = () => Exts.Vector(NormalEta[0].Sample(), NormalEta[1].Sample());
X0Hat = mEta;
DX0Hat = dEta;
}
}
static void Run(Options o, string[] args)
{
if (string.IsNullOrWhiteSpace(o.OutputFolder))
{
o.OutputFolder = Settings.Default.OutputFolder;
}
if (string.IsNullOrWhiteSpace(o.PlotsFolder))
{
o.PlotsFolder = Settings.Default.LatexFolder;
}
if (string.IsNullOrWhiteSpace(o.ScriptsFolder))
{
o.ScriptsFolder = Settings.Default.ScriptsFolder;
}
if (string.IsNullOrWhiteSpace(o.TemplatesFolder))
{
o.TemplatesFolder = Settings.Default.LatexFolder;
}
if (new[] { "sphere", "polar", "polartwo" }.Contains(o.Model))
{
#region sphere
if (o.Model == "sphere")
{
int N = o.N;
Vector <double> mX = Exts.Vector(30, 40, 100); Matrix <double> KX = Exts.Diag(30 * 30, 30 * 30, 30 * 30);
Vector <double> mNu = Exts.Vector(0, 0, 0); Matrix <double> KNu = Exts.Diag(30 * 30, Math.Pow(5 * Math.PI / 180.0, 2.0), Math.Pow(5 * Math.PI / 180.0, 2.0));
Normal[] NormalX = new Normal[3] {
new Normal(mX[0], Math.Sqrt(KX[0, 0])), new Normal(mX[1], Math.Sqrt(KX[1, 1])), new Normal(mX[2], Math.Sqrt(KX[2, 2]))
};
Normal[] NormalNu = new Normal[3] {
new Normal(mNu[0], Math.Sqrt(KNu[0, 0])), new Normal(mNu[1], Math.Sqrt(KNu[1, 1])), new Normal(mNu[2], Math.Sqrt(KNu[2, 2]))
};;
TestEnvironmentStatic testSphere = new TestEnvironmentStatic
{
Phi = x => Utils.cart2sphere(x),
InvPhi = y => Utils.sphere2cart(y),
W = () => Exts.Vector(NormalX[0].Sample(), NormalX[1].Sample(), NormalX[2].Sample()),
Nu = () => Exts.Vector(NormalNu[0].Sample(), NormalNu[1].Sample(), NormalNu[2].Sample()),
MX = mX,
KX = KX,
KNu = KNu
};
testSphere.Initialize(N, o.OutputFolder);
Vector <double> mErr;
Matrix <double> KErr;
Matrix <double> KErrTh;
Vector <double> mErr_inv;
Matrix <double> KErr_inv;
Matrix <double> KErrTh_inv;
Vector <double> mErr_lin;
Matrix <double> KErr_lin;
Matrix <double> KErrTh_lin;
Vector <double> mErr_UT;
Matrix <double> KErr_UT;
Matrix <double> KErrTh_UT;
string fileName_alldata = Path.Combine(o.OutputFolder, "test_sphere_alldata.txt");
testSphere.GenerateBundle(N, out mErr, out KErr, out KErrTh, out mErr_inv, out KErr_inv, out KErrTh_inv, out mErr_lin, out KErr_lin, out KErrTh_lin, out mErr_UT, out KErr_UT, out KErrTh_UT, fileName_alldata);
string fileName = Path.Combine(o.OutputFolder, "test_sphere.txt");
using (System.IO.StreamWriter outputfile = new System.IO.StreamWriter(fileName))
{
//outputfile.WriteLine($"P = {P}");
outputfile.WriteLine($"mErr = {mErr}");
outputfile.WriteLine($"KErr = {KErr}");
outputfile.WriteLine($"KErrTh = {KErrTh}");
//outputfile.WriteLine($"P_inv = {P_inv}");
outputfile.WriteLine($"mErr_inv = {mErr_inv}");
outputfile.WriteLine($"KErr_inv = {KErr_inv}");
outputfile.WriteLine($"KErrTh_inv = {KErrTh_inv}");
//outputfile.WriteLine($"P_lin = {P_lin}");
outputfile.WriteLine($"mErr_lin = {mErr_lin}");
outputfile.WriteLine($"KErr_lin = {KErr_lin}");
outputfile.WriteLine($"KErrTh_lin = {KErrTh_lin}");
//outputfile.WriteLine($"P_UT = {P_UT}");
outputfile.WriteLine($"mErr_UT = {mErr_UT}");
outputfile.WriteLine($"KErr_UT = {KErr_UT}");
outputfile.WriteLine($"KErrTh_UT = {KErrTh_UT}");
outputfile.Close();
}
}
#endregion
#region polar
if (o.Model == "polar")
{
int N = o.N;
Vector <double> mX = Exts.Vector(300, 400); Matrix <double> KX = Exts.Diag(30 * 30, 30 * 30);
//Vector<double> mX = Exts.Vector(30000, 40000); Matrix<double> KX = Exts.Diag(100 * 100, 100 * 100);
//Vector<double> mX = Exts.Vector(30000, 40000); Matrix<double> KX = Exts.Diag(4500 * 4500, 4500 * 4500);
Vector <double> mNu = Exts.Vector(0, 0); Matrix <double> KNu = Exts.Diag(Math.Pow(5 * Math.PI / 180.0, 2.0), 30 * 30);
Normal[] NormalX = new Normal[2] {
new Normal(mX[0], Math.Sqrt(KX[0, 0])), new Normal(mX[1], Math.Sqrt(KX[1, 1]))
};
Normal[] NormalNu = new Normal[2] {
new Normal(mNu[0], Math.Sqrt(KNu[0, 0])), new Normal(mNu[1], Math.Sqrt(KNu[1, 1]))
};;
//Console.WriteLine(mX.ToLine());
TestEnvironmentStatic testPolar = new TestEnvironmentStatic
{
Phi = x => Utils.cart2pol(x),
InvPhi = y => Utils.pol2cart(y),
W = () => Exts.Vector(NormalX[0].Sample(), NormalX[1].Sample()),
Nu = () => Exts.Vector(NormalNu[0].Sample(), NormalNu[1].Sample()),
MX = mX,
KX = KX,
KNu = KNu
};
testPolar.Initialize(N, o.OutputFolder);
Vector <double> mErr;
Matrix <double> KErr;
Matrix <double> KErrTh;
Vector <double> mErr_inv;
Matrix <double> KErr_inv;
Matrix <double> KErrTh_inv;
Vector <double> mErr_lin;
Matrix <double> KErr_lin;
Matrix <double> KErrTh_lin;
Vector <double> mErr_UT;
Matrix <double> KErr_UT;
Matrix <double> KErrTh_UT;
string fileName_alldata = Path.Combine(o.OutputFolder, "test_polar_alldata.txt");
testPolar.GenerateBundle(N, out mErr, out KErr, out KErrTh, out mErr_inv, out KErr_inv, out KErrTh_inv, out mErr_lin, out KErr_lin, out KErrTh_lin, out mErr_UT, out KErr_UT, out KErrTh_UT, fileName_alldata);
string fileName = Path.Combine(o.OutputFolder, "test_polar.txt");
using (System.IO.StreamWriter outputfile = new System.IO.StreamWriter(fileName))
{
//outputfile.WriteLine($"P = {P}");
outputfile.WriteLine($"mErr = {mErr}");
outputfile.WriteLine($"KErr = {KErr}");
outputfile.WriteLine($"KErrTh = {KErrTh}");
//outputfile.WriteLine($"P_inv = {P_inv}");
outputfile.WriteLine($"mErr_inv = {mErr_inv}");
outputfile.WriteLine($"KErr_inv = {KErr_inv}");
outputfile.WriteLine($"KErrTh_inv = {KErrTh_inv}");
//outputfile.WriteLine($"P_lin = {P_lin}");
outputfile.WriteLine($"mErr_lin = {mErr_lin}");
outputfile.WriteLine($"KErr_lin = {KErr_lin}");
outputfile.WriteLine($"KErrTh_lin = {KErrTh_lin}");
//outputfile.WriteLine($"P_UT = {P_UT}");
outputfile.WriteLine($"mErr_UT = {mErr_UT}");
outputfile.WriteLine($"KErr_UT = {KErr_UT}");
outputfile.WriteLine($"KErrTh_UT = {KErrTh_UT}");
outputfile.Close();
}
}
#endregion
#region polartwo
if (o.Model == "polartwo")
{
int N = o.N;
Vector <double> secondpoint = Exts.Vector(-10000, 10000);
Vector <double> mX = Exts.Vector(30000, 40000); Matrix <double> KX = Exts.Diag(2000 * 2000, 2000 * 2000);
Vector <double> mNu = Exts.Vector(0, 0, 0, 0);
Matrix <double> KNu = Exts.Diag(Math.Pow(0.1 * Math.PI / 180.0, 2.0),
50 * 50,
Math.Pow(0.1 * Math.PI / 180.0, 2.0),
50 * 50);
Normal[] NormalX = new Normal[2] {
new Normal(mX[0], Math.Sqrt(KX[0, 0])), new Normal(mX[1], Math.Sqrt(KX[1, 1]))
};
Normal[] NormalNu = new Normal[4] {
new Normal(mNu[0], Math.Sqrt(KNu[0, 0])),
new Normal(mNu[1], Math.Sqrt(KNu[1, 1])),
new Normal(mNu[2], Math.Sqrt(KNu[2, 2])),
new Normal(mNu[3], Math.Sqrt(KNu[3, 3]))
};
//Console.WriteLine(mX.ToLine());
TestEnvironmentStatic testPolar = new TestEnvironmentStatic
{
Phi = x => Exts.Stack(Utils.cart2pol(x), Utils.cart2pol(x - secondpoint)),
InvPhi = y => Exts.Stack(Utils.pol2cart(Exts.Vector(y[0], y[1])), Utils.pol2cart(Exts.Vector(y[2], y[3])) + secondpoint),
W = () => Exts.Vector(NormalX[0].Sample(), NormalX[1].Sample()),
Nu = () => Exts.Vector(NormalNu[0].Sample(), NormalNu[1].Sample(), NormalNu[2].Sample(), NormalNu[3].Sample()),
//Nu = () => Exts.Vector(0,0,0,0),
MX = mX,
KX = KX,
KNu = KNu
};
testPolar.Initialize(N, o.OutputFolder);
Vector <double> mErr;
Matrix <double> KErr;
Matrix <double> KErrTh;
Vector <double> mErr_inv;
Matrix <double> KErr_inv;
Matrix <double> KErrTh_inv;
Vector <double> mErr_lin;
Matrix <double> KErr_lin;
Matrix <double> KErrTh_lin;
Vector <double> mErr_UT;
Matrix <double> KErr_UT;
Matrix <double> KErrTh_UT;
string fileName_alldata = Path.Combine(o.OutputFolder, "test_polartwo_alldata.txt");
testPolar.GenerateBundle(N, out mErr, out KErr, out KErrTh, out mErr_inv, out KErr_inv, out KErrTh_inv, out mErr_lin, out KErr_lin, out KErrTh_lin, out mErr_UT, out KErr_UT, out KErrTh_UT, fileName_alldata);
string fileName = Path.Combine(o.OutputFolder, "test_polartwo.txt");
using (System.IO.StreamWriter outputfile = new System.IO.StreamWriter(fileName))
{
//outputfile.WriteLine($"P = {P}");
outputfile.WriteLine($"mErr = {mErr}");
outputfile.WriteLine($"KErr = {KErr}");
outputfile.WriteLine($"KErrTh = {KErrTh}");
//outputfile.WriteLine($"P_inv = {P_inv}");
outputfile.WriteLine($"mErr_inv = {mErr_inv}");
outputfile.WriteLine($"KErr_inv = {KErr_inv}");
outputfile.WriteLine($"KErrTh_inv = {KErrTh_inv}");
//outputfile.WriteLine($"P_lin = {P_lin}");
outputfile.WriteLine($"mErr_lin = {mErr_lin}");
outputfile.WriteLine($"KErr_lin = {KErr_lin}");
outputfile.WriteLine($"KErrTh_lin = {KErrTh_lin}");
//outputfile.WriteLine($"P_UT = {P_UT}");
outputfile.WriteLine($"mErr_UT = {mErr_UT}");
outputfile.WriteLine($"KErr_UT = {KErr_UT}");
outputfile.WriteLine($"KErrTh_UT = {KErrTh_UT}");
outputfile.Close();
}
}
#endregion
}
else
{
TestEnvironmentVector testEnv = new TestEnvironmentVector();
if (o.Model == "cubic")
{
testEnv = new TestCubicSensorScalar(o.DW, o.DNu);
}
if (o.Model == "invprop-good")
{
testEnv = new TestInverseProportionGoodScalar(o.Bound, o.DW, o.DNu);
}
if (o.Model == "invprop-bad")
{
testEnv = new TestInverseProportionBadScalar(o.Bound, o.DW, o.DNu);
}
if (o.Model == "logreg-simple")
{
testEnv = new TestLogisticModelScalar(o.Bound, o.DW, o.DNu);
}
if (o.Model == "logreg-zero")
{
testEnv = new TestLogisticModelZeroScalar(o.Bound, o.DW, o.DNu);
}
if (o.Model == "logreg-uniform")
{
testEnv = new TestLogisticModelUniformNoiseScalar();
}
if (o.Model == "samplereg")
{
testEnv = new TestSampledRegression(o.DNu);
}
if (o.Model == "switchingobs")
{
testEnv = new TestSwitchingObservations(o.DNu);
}
if (o.Model == "switchingobsident")
{
switch (o.IdentNumber)
{
case 1: testEnv = new AnotherTestSwitchingObservationsIdentification(o.DNu); break;
case 2: testEnv = new YetAnotherTestSwitchingObservationsIdentification(o.DNu); break;
case 3: testEnv = new HopefullyTheLastTestSwitchingObservationsIdentification(o.DNu); break;
default: testEnv = new TestSwitchingObservationsIdentification(o.DNu); break;
}
}
if (o.Model == "simpleident")
{
testEnv = new TestSimpleIdentification();
}
string CMNFFileName = Path.Combine(o.OutputFolder, "cmnf.params");
if (!string.IsNullOrWhiteSpace(o.CMNFFileName))
{
CMNFFileName = o.CMNFFileName;
}
string BCMNFFileName = Path.Combine(o.OutputFolder, "bcmnf.params");
if (!string.IsNullOrWhiteSpace(o.BCMNFFileName))
{
BCMNFFileName = o.BCMNFFileName;
}
string UKFFileName = Path.Combine(o.OutputFolder, "ukf.params");
if (!string.IsNullOrWhiteSpace(o.UKFFileName))
{
UKFFileName = o.UKFFileName;
}
string UKFOptStepwiseNMFileName = Path.Combine(o.OutputFolder, "ukfoptstepwiseNM.params");
if (!string.IsNullOrWhiteSpace(o.UKFStepwiseNelderMeadFileName))
{
UKFOptStepwiseNMFileName = o.UKFStepwiseNelderMeadFileName;
}
string UKFOptIntegralNMFileName = Path.Combine(o.OutputFolder, "ukfoptintegralNM.params");
if (!string.IsNullOrWhiteSpace(o.UKFIntegralNelderMeadFileName))
{
UKFOptIntegralNMFileName = o.UKFIntegralNelderMeadFileName;
}
string UKFOptStepwiseRandFileName = Path.Combine(o.OutputFolder, "ukfoptstepwiserand.params");
if (!string.IsNullOrWhiteSpace(o.UKFStepwiseRandomShootFileName))
{
UKFOptStepwiseRandFileName = o.UKFStepwiseRandomShootFileName;
}
string UKFOptIntegralRandFileName = Path.Combine(o.OutputFolder, "ukfoptintegralrand.params");
if (!string.IsNullOrWhiteSpace(o.UKFIntegralRandomShootFileName))
{
UKFOptIntegralRandFileName = o.UKFIntegralRandomShootFileName;
}
List <(FilterType, string)> filters = new List <(FilterType, string)>();
if (o.CMNF)
{
filters.Add((FilterType.CMNF, CMNFFileName));
}
if (o.BCMNF)
{
testEnv.Alpha = testEnv.Xi;
testEnv.Gamma = (t, x, y) => y;
filters.Add((FilterType.BCMNF, BCMNFFileName));
}
if (o.MCMNF)
{
testEnv.nMCMNF = o.MCMNFTrainCount;
filters.Add((FilterType.MCMNF, string.Empty));
}
if (o.UKF)
{
filters.Add((FilterType.UKFNoOptimization, UKFFileName));
}
if (o.UKFStepwiseNelderMead)
{
filters.Add((FilterType.UKFStepwise, UKFOptStepwiseNMFileName));
}
if (o.UKFIntegralNelderMead)
{
filters.Add((FilterType.UKFIntegral, UKFOptIntegralNMFileName));
}
if (o.UKFStepwiseRandomShoot)
{
filters.Add((FilterType.UKFStepwiseRandomShoot, UKFOptStepwiseRandFileName));
}
if (o.UKFIntegralRandomShoot)
{
filters.Add((FilterType.UKFIntegralRandomShoot, UKFOptIntegralRandFileName));
}
if (o.EKF)
{
filters.Add((FilterType.EKF, string.Empty));
}
if (o.Dummy)
{
filters.Add((FilterType.Dummy, string.Empty));
}
using (System.IO.StreamWriter outputfile = new System.IO.StreamWriter(Path.Combine(o.OutputFolder, "parameters.txt"), true))
{
outputfile.WriteLine($"{DateTime.Now}\t{string.Join(" ", args)}");
outputfile.Close();
}
if (o.Bulk)
{
testEnv.GenerateBundleSamples(o.T, o.TrainCount, o.OutputFolder);
}
else
{
testEnv.Initialize(o.T, o.TrainCount, o.OutputFolder, filters, o.Save, o.Load);
if (o.Aggregate)
{
testEnv.Aggregate(o.OutputFolder, o.OutputFolder, !o.NoBin, !o.NoText);
}
if (!o.Skip)
{
testEnv.GenerateBundles(o.BundleCount, o.TestCount, o.OutputFolder, o.Parallel, o.ParallelismDegree, !o.NoBin, !o.NoText);
//if (o.BundleCount > 1)
// testEnv.GenerateBundles(o.BundleCount, o.TestCount, o.OutputFolder, o.Parallel, o.ParallelismDegree);
//else
// testEnv.GenerateBundle(o.TestCount, o.OutputFolder);
if (o.SamplesCount == 1)
{
testEnv.GenerateOne(o.OutputFolder);
}
else
{
for (int i = 0; i < o.SamplesCount; i++)
{
testEnv.GenerateOne(o.OutputFolder, i);
}
}
//testEnv.GenerateReport(o.TemplatesFolder, o.PlotsFolder);
}
if (!o.NoPython)
{
testEnv.ProcessResults(o.OutputFolder, o.ScriptsFolder, o.PlotsFolder);
}
}
}
}
