public OutputData Run()
{
int i, j, numOfObservations = observations.numOfObservations;
double T = area.t.ElementAt(1) - area.t.ElementAt(0), t0=area.t.ElementAt(0);
double[] moments;
double[] arobservations;
double EPSILON = 1e-10;
Random rnd = new Random();
OutputData result = new OutputData();
List<double> positiveMoments = new List<double>();
List<double> positiveObservations = new List<double>();
moments = new double[numOfObservations];
arobservations = new double[numOfObservations];
//for (i = 0; i < numOfObservations; ++i)//this code should be removed by input from graph/datagrid
//{
// noise = (rnd.NextDouble() - 0.5) * T / compression; // add some noise
// moments[i] = T * rnd.NextDouble() - t0;
// arobservations[i] = function(moments[i]) + noise;
//}
for (i = 0; i < numOfObservations; ++i)//this code should be removed by input from graph/datagrid
{
moments[i] = observations.getMoments()[i];
arobservations[i] = observations.getYArray()[i];
}
// delete all initial observations
for (i = 0; i < numOfObservations; ++i)
if (moments[i] > EPSILON) // EPSILON threshold for observations
{
positiveMoments.Add(moments[i]);
positiveObservations.Add(arobservations[i]);
}
numOfObservations = positiveMoments.Count;
/* PROBLEM 3: u(t), X0 are factors to control.
* G0_*u0_ + Gu_*u_ = X_
* G0_ = str(G0(tm0), m = 1, ..., M0); <- numOfObservations
* Gu_ = str(Gu(tmu), m = 1, ..., Mu); <- numOfObservations
* X_ - observations;
* G0(t) = col(G1(t))
* G1(t') = col(g(t-t')), t = ti, t' < 0
* Gu(t) = col(G2(t))
* G2(t') = col(g(t-t')), t = ti, t' > 0
*/
double[,] Gu_ = new double[numOfObservations, numOfObservations];
double[,] G0_ = new double[numOfObservations, numOfObservations];
double[,] P = new double[numOfObservations, numOfObservations];
for (i = 0; i < numOfObservations; ++i)
{
for (j = 0; j < numOfObservations; ++j)
{
Gu_[i, j] = transfer(positiveMoments[i] - positiveMoments[j]);
G0_[i, j] = transfer(positiveMoments[i] + positiveMoments[j]); // take imaginary positiveObservations from [-x1, -x0]
}
}
// P = G0_ * (G0_)^T + Gu_ * (Gu_)^T
double temp = 0;
int k;
for (i = 0; i < numOfObservations; ++i)
{
for (j = 0; j < numOfObservations; ++j)
{
temp = 0;
for (k = 0; k < numOfObservations; ++k)
temp += G0_[i, k] * G0_[j, k] + Gu_[i, k] * Gu_[j, k];
P[i, j] = temp;
}
}
// find P+
double[,] invP = new double[numOfObservations, numOfObservations];
AdditionalFunctions.inverseMatrix(P, invP, numOfObservations);
result.resize(numOfObservations);
// u_ = (Gu_)^T * P+ * X_
// u0_ = (G0_)^T * P+ * X_
// u[i] = sum(j) (X[j] * sum(k) (G[k, i] * P+[k, j]))
for (i = 0; i < numOfObservations; ++i)
{
result.u[i] = 0;
result.u0[i] = 0;
for (j = 0; j < numOfObservations; ++j)
{
temp = 0;
for (k = 0; k < numOfObservations; ++k)
temp += Gu_[k, i] * invP[k, j];
result.u[i] += temp * positiveObservations[j];
temp = 0;
for (k = 0; k < numOfObservations; ++k)
temp += G0_[k, i] * invP[k, j];
result.u0[i] += temp * positiveObservations[j];
}
}
// test solution
for (i = 0; i < numOfObservations; ++i)
{
temp = 0;
for (j = 0; j < numOfObservations; ++j)
temp += G0_[i, j] * result.u0[j] + Gu_[i, j] * result.u[j];
result.x[i] = temp;
result.t[i] = positiveMoments[i];
result.func[i] = function(result.t[i]);
}
result.isSolved = true;
return result;
}
public OutputData Run()
{
int i, j, numOfObservations=observations.numOfObservations;
double T = area.t.ElementAt(1) - area.t.ElementAt(0), t0=area.t.ElementAt(0);
double[] moments;
double[] arobservations;
Random rnd = new Random();
OutputData result = new OutputData();
moments = new double[numOfObservations+1];
arobservations = new double[numOfObservations+1];
// initial observation
moments[0] = t0;
if (!observations.findObservationAt(t0, out arobservations[0]))
arobservations[0] = function(moments[0]) + (rnd.NextDouble() - 0.5) * T / 20;
//moments = (moments.ToList<double>().AddRange(observations.getMoments())).ToArray();
//arobservations = (arobservations.ToList<double>().AddRange(observations.getYArray())).ToArray();
for (i = 0; i < numOfObservations; ++i)//this code should be removed by input from graph/datagrid
{
moments[i + 1] = observations.getMoments()[i];
arobservations[i + 1] = observations.getYArray()[i];
}
++numOfObservations;
/* PROBLEM 1: u(t) is unknown.
* G0_*u0_ + Gu_*u_ = X_
* G0_ = str(G0(tm0), m = 1, ..., M0); <- numOfObservations
* Gu_ = str(Gu(tmu), m = 1, ..., Mu); <- numOfObservations
* X_ - observations;
* G0(t) = col(G1(t))
* G1(t') = col(g(t-t')), t = ti, t' < 0
* Gu(t) = col(G2(t))
* G2(t') = col(g(t-t')), t = ti, t' > 0
*/
double[,] Gu_ = new double[numOfObservations, numOfObservations - 1];
double[,] G0_ = new double[numOfObservations, numOfObservations - 1];
double[,] P = new double[numOfObservations, numOfObservations];
for (i = 0; i < numOfObservations; ++i)
{
for (j = 1; j < numOfObservations; ++j)
{
Gu_[i, j - 1] = transfer(moments[i] - moments[j]);
G0_[i, j - 1] = transfer(moments[i] + moments[j]); // take imaginary observations from [-x1, -x0]
}
}
// P = G0_ * (G0_)^T + Gu_ * (Gu_)^T
double temp = 0;
int k;
for (i = 0; i < numOfObservations; ++i)
{
for (j = 0; j < numOfObservations; ++j)
{
temp = 0;
for (k = 0; k < numOfObservations - 1; ++k)
temp += G0_[i, k] * G0_[j, k] + Gu_[i, k] * Gu_[j, k];
P[i, j] = temp;
}
}
// find P+
double[,] invP = new double[numOfObservations, numOfObservations];
AdditionalFunctions.inverseMatrix(P, invP, numOfObservations);
result.resize(numOfObservations);
// u_ = (Gu_)^T * P+ * X_
// u0_ = (G0_)^T * P+ * X_
// u[i] = sum(j) (X[j] * sum(k) (G[k, i] * P+[k, j]))
for (i = 0; i < numOfObservations - 1; ++i)
{
result.u[i] = 0;
result.u0[i] = 0;
for (j = 0; j < numOfObservations; ++j)
{
temp = 0;
for (k = 0; k < numOfObservations; ++k)
temp += Gu_[k, i] * invP[k, j];
result.u[i] += temp * arobservations[j];
temp = 0;
for (k = 0; k < numOfObservations; ++k)
temp += G0_[k, i] * invP[k, j];
result.u0[i] += temp * arobservations[j];
}
}
// test solution
for (i = 0; i < numOfObservations; ++i)
{
temp = 0;
for (j = 0; j < numOfObservations - 1; ++j)
temp += G0_[i, j] * result.u0[j] + Gu_[i, j] * result.u[j];
result.x[i] = temp;
result.t[i] = moments[i];
result.func[i] = function(result.t[i]);
}
result.isSolved = true;
return result;
}
private void DisplayToDataGrid(OutputData result)
{
List<OutputPoint> points = result.ResultPoints();
var Points = CollectionViewSource.GetDefaultView(points);
List<Tuple<double, double>> pairsU = new List<Tuple<double, double>>();
for (int i = 0; i < result.x.Count; i++)
pairsU.Add(new Tuple<double, double>(result.t[i], result.u[i]));
pairsU = pairsU.OrderBy(p => p.Item1).ToList();
List<double> xAxisU = new List<double>();
List<double> yAxisU = new List<double>();
foreach (var pair in pairsU)
{
xAxisU.Add(pair.Item1);
yAxisU.Add(pair.Item2);
}
List<Tuple<double, double>> pairsX = new List<Tuple<double, double>>();
for (int i = 0; i < result.x.Count; i++)
pairsX.Add(new Tuple<double, double>(result.t[i], result.x[i]));
pairsX = pairsX.OrderBy(p => p.Item1).ToList();
List<double> xAxisX = new List<double>();
List<double> yAxisX = new List<double>();
foreach (var pair in pairsX)
{
xAxisX.Add(pair.Item1);
yAxisX.Add(pair.Item2);
}
List<Tuple<double, double>> pairsF = new List<Tuple<double, double>>();
for (int i = 0; i < result.x.Count; i++)
pairsF.Add(new Tuple<double, double>(result.t[i], result.func[i]));
pairsF = pairsF.OrderBy(p => p.Item1).ToList();
List<double> xAxisF = new List<double>();
List<double> yAxisF = new List<double>();
foreach (var pair in pairsF)
{
xAxisF.Add(pair.Item1);
yAxisF.Add(pair.Item2);
}
//Filling Data Grid
DataGridOutputResults.ItemsSource = Points;
//Drawing plot
DrawingPlot(outputPlotter, xAxisU, yAxisU, "Керування", Brushes.Black);
DrawingPlot(outputPlotter, xAxisX, yAxisX, "Стан системи", Brushes.Red);
DrawingPlot(comparePlotter, xAxisX, yAxisX, "Стан системи", Brushes.Red);
DrawingPlot(comparePlotter, xAxisF, yAxisF, "Точне значення функції", Brushes.Blue);
}
private void DisplayToMessageBox(OutputData result)
{
MessageBox.Show(result.ToString());
}
public OutputData Analyse()
{
OutputData result = new OutputData();
if (OperatoOrTransferInput == OperatoOrTransferInputEnum.OPERATOR)
ConvertOperatorToTransfer();
if (ProblemType == ProblemTypeEnum.CONTROL_PROBLEM)
{
if (ObservationType == ObservationTypeEnum.CONTINIOUS)
{
result.isSolved = false;
}
else
{
if (ControlType == ControlTypeEnum.CONTINIOUS)
{
result.isSolved = false;
}
else
{
if (Dimension == DimensionEnum._2D)
{
if (ControlFactorType == ControlFactorTypeEnum.CONTROL)
{
Contol2dFirst Solver = new Contol2dFirst(this);
result = Solver.Run();
}
else if (ControlFactorType == ControlFactorTypeEnum.INITIAL_PERTRUBATIONS)
{
Contol2dSecond Solver = new Contol2dSecond(this);
result = Solver.Run();
}
else if (ControlFactorType == ControlFactorTypeEnum.CONTROL_AND_INITIAL_PERTRUBATIONS)
{
Contol2dThird Solver = new Contol2dThird(this);
result = Solver.Run();
}
else
{
result.isSolved=false;
}
}
else
{
result.isSolved = false;
}
}
}
}
else
{
result.isSolved = false;
}
return result;
}
