private void PopulatePWT91DataTable() { DataSet ds = new DataSet(); ds.ReadXml(new StringReader(Properties.Resources.pwt91)); dt = ds.Tables[0]; CountriesViewModel.CountryItems.Clear(); string[] columns = { "countrycode", "country", "currency_unit" }; foreach (DataRow dr in dt.DefaultView.ToTable(true, columns).Rows) { CountriesViewModel.CountryItems.Add(CountryVM.NewCountryVM(PWTCountry.NewCountry(dt.Select($"countrycode = '{dr["countrycode"]}'")))); } }
internal static PWTCountry NewCountry(DataRow[] dataRows) { PWTCountry pwtc = new PWTCountry { Observations = dataRows, CountryCode = dataRows[0]["countrycode"]?.ToString() ?? "", CountryName = dataRows[0]["country"]?.ToString() ?? "", CurrencyUnit = dataRows[0]["currency_unit"]?.ToString() ?? "" }; pwtc.SavingsRateHT = new SortedDictionary <int, double>(); pwtc.LGrowthRateHT = new SortedDictionary <int, double>(); pwtc.AGrowthRateHT = new SortedDictionary <int, double>(); pwtc.CalculateRates(); return(pwtc); }
private void CountriesListView_SelectionChanged(object sender, SelectionChangedEventArgs e) { KalmanFilterButton.IsEnabled = true; country = ((CountryVM)(sender as ListView).SelectedItem).CountryObject; PlotCountryValues(); }
private void KalmanFilterButton_Click(object sender, RoutedEventArgs e) { if (CountriesListView.SelectedItem == null) { return; } PWTCountry country = (CountriesListView.SelectedItem as CountryVM).CountryObject; SortedDictionary <int, double> kalmanDataSet = country.SavingsRateHT; int minYear = kalmanDataSet.Keys.Min(); int maxYear = kalmanDataSet.Keys.Max(); if (!double.TryParse(TimeStepTerm.Text, out double dt) || dt < 0) { MessageBox.Show("invalid time step"); return; } int numXVals = (int)((maxYear - minYear) / dt) + 1; // time steps MathMatrix t = Sequences.SteppedSequence(minYear, maxYear, dt); // state matrix MathMatrix xt = MathMatrix.CreateMatrix(3, numXVals, 0); double[] timeArray = t.RowVectorArray(0); for (int colidx = 0; colidx < t.ColCount; ++colidx) { if (Math.Floor(timeArray[colidx]) == Math.Ceiling(timeArray[colidx]) && country.AGrowthRateHT.ContainsKey((int)timeArray[colidx])) { int key = (int)timeArray[colidx]; xt[0, colidx] = country.AGrowthRateHT[key]; xt[1, colidx] = country.SavingsRateHT[key]; xt[2, colidx] = country.LGrowthRateHT[key]; } else if (colidx > 0) { xt[0, colidx] = xt[0, colidx - 1]; xt[1, colidx] = xt[1, colidx - 1]; xt[2, colidx] = xt[2, colidx - 1]; } } // state matrix // x(k) = Fx(k-1) + Gu(k-1) which can be seen below in Kalman filter loop. MathMatrix x = MathMatrix.CreateMatrix(3, numXVals, 0); // process matrix moves state matrix from state k to k + 1 MathMatrix F = MatrixOperations.Identity(3); MathMatrix FT = F; // control matrix MathMatrix u = MathMatrix.CreateMatrix(1, 1, 0); MathMatrix G = MathMatrix.CreateMatrix(3, 1, new double[] { 0, 0, 0 }); // state error covariance matrix if (!double.TryParse(CovarianceTerm.Text, out double covterm)) { MessageBox.Show("Covariance term needs to be a double value."); return; } //MathMatrix P = MathMatrix.CreateMatrix(3, 3, new double[] { -0.1, 0.05, -0.1, 0.001, 0.01, -0.005, -0.005, 0.15, -0.05 }); MathMatrix P = MathMatrix.CreateMatrix(3, 3, new double[] { covterm, 0, 0, 0, covterm, 0, 0, 0, covterm }); // observation matrix MathMatrix H = MathMatrix.CreateMatrix(1, 3, new double[] { 1, 1, 1 }); MathMatrix HT = MatrixOperations.Transpose(H); // process noise covariance matrix MathMatrix Q = MathMatrix.CreateMatrix(3, 3, new double[] { -0.01, 0.05, -0.1, 0.001, -0.01, -0.005, -0.005, 0.15, -0.05 }); MathMatrix I = MatrixOperations.Identity(3); // measurement noise covariance matrix MathMatrix R = MathMatrix.CreateMatrix(1, 1, 3); MathMatrix sqrtR = MatrixOperations.Sqrt_Elmtwise(R); // measurement noise MathMatrix v = sqrtR * Distributions.Normal(numXVals); // observation / measurement // y(k) = Hxt(k) + v(k); MathMatrix y = H * xt + v; // Kalman filter for (int k = 0; k < numXVals; ++k) { x.AssignColumn(F * xt.ColumnVector(k) + G * u, k); P = F * P * FT + Q; // HACK HERE SINCE WE DO NOT YET HAVE MATRIX INVERSION. MathMatrix Knumerator = P * HT; MathMatrix Kdenominator = (H * P * HT + R); Kdenominator[0, 0] = 1 / Kdenominator[0, 0]; MathMatrix K = Knumerator * Kdenominator; x.AssignColumn(x.ColumnVector(k) + K * (y.ColumnVector(k) - H * x.ColumnVector(k)), k); P = (I - K * H) * P; } ResultsPlot.ClearPlotArea(clearPlotData: true); int kalmanrowidx = KalmanFilterPlotType == "n" ? 2 : KalmanFilterPlotType == "s" ? 1 : KalmanFilterPlotType == "g" ? 0 : -1; if (kalmanrowidx == -1) { MessageBox.Show("A filter quantity must be selected."); return; } // GO THROUGH THE ARRAYS AND DROP CORRESPONDING NAN OR INFINITY ENTRIES FROM X AND XT. double[] numsOnlyVec = x.RowVectorArray(kalmanrowidx).Where(p => !double.IsNaN(p) && !double.IsInfinity(p)).ToArray(); double minY = new double[] { numsOnlyVec.Min(), xt.RowVectorArray(kalmanrowidx).Min() }.Min(); double maxY = new double[] { numsOnlyVec.Max(), xt.RowVectorArray(kalmanrowidx).Max() }.Max(); ap.YLabel = YAxisLabel.NewAxisLabel(KalmanFilterPlotQtyDict[KalmanFilterPlotType], 0.5, 15, ylp); ap.XLabel = XAxisLabel.NewAxisLabel("Year", minY < 0 ? 0.05 : 0.5, 15, minY < 0 ? xlp2 : xlp); ResultsPlot.SetAxes(minYear, maxYear, minY, maxY, ap, drawHorAxisAtY0: minY < 0); ResultsPlot.SetPlotGridLines(20, 20); PointCollection pc = new PointCollection(); PointCollection pc2 = new PointCollection(); for (int idx = 0; idx < numsOnlyVec.Length; ++idx) { pc.Add(new Point(t[0, idx], numsOnlyVec[idx])); pc2.Add(new Point(t[0, idx], xt[kalmanrowidx, idx])); } ResultsPlot.PlotPoints2D($"KalmanFiltered_{country.CountryCode}_{kalmanrowidx}_Points", pc, dpp); ResultsPlot.PlotCurve2D($"KalmanFiltered_{country.CountryCode}_{kalmanrowidx}_Curve", pc2, cp2); }