private void ShowResults()
{
if (rbBestNetwork.Checked)
{
mSelectedActivationNetwork = mBestActivationNetwork;
}
if (rbBestTestNetwork.Checked)
{
mSelectedActivationNetwork = mBestTestActivationNetwork;
}
if (rbGenetic.Checked)
{
mSelectedActivationNetwork = mBestGeneticActivationNetwork;
}
try
{
TVec dy = null;
TVec dx = null;
fittedChart.Series.Clear();
fittedChart.Series.Add(new Steema.TeeChart.Styles.FastLine());
fittedChart.Series.Add(new Steema.TeeChart.Styles.FastLine());
fittedChart.Series[0].Title = "наблюдения";
fittedChart.Series[1].Title = "прогнози";
fittedChart.Axes.Left.Title.Text = "Y";
fittedChart.Axes.Bottom.Title.Text = "";
fittedChart.Axes.Left.Automatic = true;
Vector timeseries = new Vector((TestEnd-ExtractBegin) + 1);
Vector abs = new Vector((TestEnd - ExtractBegin) + 1);
int? dependent_variable_id = 0;
foreach (int? dep in this.DependentVariables.Keys)
{
dependent_variable_id = dep;
}
MainDataSetTableAdapters.VARIABLESTableAdapter adptVariables = new Plig.TimeSeries.Client.MainDataSetTableAdapters.VARIABLESTableAdapter();
mindep = adptVariables.MinObservationValue(ExtractBegin, TestEnd, dependent_variable_id.Value).Value;
factordep = 1.7 / (adptVariables.MaxObservationValue(ExtractBegin, TestEnd, dependent_variable_id.Value).Value - adptVariables.MinObservationValue(ExtractBegin, TestEnd, dependent_variable_id.Value).Value);
int cnt = ExtractBegin;
foreach (KeyValuePair<int, double?> d in this.VariableObservations(dependent_variable_id.Value))
{
if (d.Key >= ExtractBegin && d.Key <= TestEnd)
{
try
{
timeseries.Values[cnt - ExtractBegin] = d.Value.Value;
}
catch
{
timeseries.Values[cnt - ExtractBegin] = 0.0;
}
abs.Values[cnt - ExtractBegin] = cnt;
cnt++;
}
}
dy = timeseries;
dx = abs;
Dew.Math.Tee.TeeChart.DrawValues(dx, dy, fittedChart.Series[0], false);
// Извличаме стойностите на независимите променливи
TVec dy1 = null;
Vector timeseries1 = new Vector(TestEnd - ExtractBegin + 1);
int independent_vars = this.IndependentVariables.Count;
double[] tuple = new double[independent_vars];
for (int i = ExtractBegin; i <= TestEnd; i++)
{
int cnt1 = 0;
foreach (int variable_id in this.IndependentVariables.Keys)
{
double factor = 1.7 / ( (this.SeriesInfo[variable_id]).Max - (this.SeriesInfo[variable_id]).Min);
tuple[cnt1] = (this.SeriesInfo[variable_id].Values[i].Value - (this.SeriesInfo[variable_id]).Min) * factor - 0.85;
cnt1++;
}
double value = (mSelectedActivationNetwork.Compute(tuple)[0] + 0.85) / factordep + mindep;
timeseries1.Values[i - ExtractBegin] = value;
}
dy1 = timeseries1;
Dew.Math.Tee.TeeChart.DrawValues(dx, dy1, fittedChart.Series[1], false);
residuals = new TVec();
residuals.Length = timeseries1.Length;
for (int i = 0; i < residuals.Length; i++)
{
residuals.Values[i] = timeseries1.Values[i] - timeseries.Values[i];
}
Vector Bins = new Vector(0);
Vector Freq = new Vector(0);
chartResiduals.Series[0].Title = "остатъци";
chartResiduals.Series[1].Title = "Нормално разпределение";
chartResiduals.Axes.Left.Title.Text = "Y";
chartResiduals.Axes.Bottom.Title.Text = "";
chartResiduals.Axes.Left.Automatic = true;
Statistics.Histogram(residuals, 20, Freq, Bins, true);
Dew.Math.Tee.TeeChart.DrawValues(Bins, Freq, chartResiduals.Series[0], false);
Vector xdense = new Vector(0);
Matrix tmpmtx = new Matrix(0, 0);
Vector x = new Vector(0);
Vector bell = new Vector(0);
// get values for Bell curve
double mean = residuals.Mean();
double stddev = residuals.StdDev(mean);
// 500 calc points
xdense.Length = 500;
xdense.Ramp(Bins.Min() , (Bins.Max() - Bins.Min()) * 0.005);
Dew.Math.Units.Probabilities.NormalPDF(xdense, mean, stddev, bell);
chartResiduals.Series[1].GetHorizAxis.Automatic = false;
chartResiduals.Series[1].GetHorizAxis.SetMinMax(residuals.Min(), residuals.Max());
Dew.Math.Tee.TeeChart.DrawValues(xdense, bell, chartResiduals.Series[1], false);
double R2 = CalculateR2(dy, dy1);
rchTests.Clear();
rchTests.SelectionColor = Color.Red;
rchTests.AppendText("Тестове");
rchTests.SelectionColor = SystemColors.InfoText; ;
rchTests.AppendText("\nНевронна мрежа с параметри: ");
rchTests.AppendText(String.Format("\nВходящ слой: {0} неврона", mSelectedActivationNetwork.InputsCount));
rchTests.AppendText(String.Format("\nСкрит слой: {0} неврона", mSelectedActivationNetwork[0].NeuronsCount));
rchTests.SelectionColor = Color.Red;
rchTests.AppendText("\nСтатистически параметри");
rchTests.SelectionColor = SystemColors.InfoText; ;
rchTests.AppendText(String.Format("\nCalculated R2: {0:F6}", R2));
rchTests.AppendText("\n\nАнализ на независимите променливи");
rchTests.AppendText("\nЗначимост");
double max_spread = 0.0;
foreach (int var_id in IndependentVariables.Keys)
{
Vector vec = IndepVar(var_id);
if (max_spread < vec.Max() - vec.Min())
{
max_spread = vec.Max() - vec.Min();
}
}
int current = 1;
foreach (int var_id in IndependentVariables.Keys)
{
Vector vec = IndepVar(var_id);
double spread = vec.Max() - vec.Min();
String result = String.Format("{0,-3}. {1,25}: {2,10} | {3}", current, IndependentVariables[var_id], spread, Stars( (int)Math.Floor((10*spread)/ max_spread)));
rchTests.AppendText("\n" + result);
current++;
}
rchTests.AppendText("\n\nDurbin-Watson тест за наличие на автоколинеарност в остатъците");
rchTests.AppendText(String.Format("\nDW: {0}", Utils.DurbinWatson((Vector)residuals)));
}
catch { };
}
