public Predictor GetPredictor(int selected_index)
{
if (selected_index == 0)
{
ARMAPredictor p = ARMA;
int result = 0;
if (int.TryParse(txtAROrder_ARMA.Text, out result))
{
p.AROrder = result;
}
if (int.TryParse(txtMAOrder_ARMA.Text, out result))
{
p.MAOrder = result;
}
if (int.TryParse(txtLDSIter_ARMA.Text, out result))
{
p.LDSIters = result;
}
if (int.TryParse(txtOptIter_ARMA.Text, out result))
{
p.OptIters = result;
}
return(mARMA);
}
else if (selected_index == 1)
{
MLPPredictor p = MLP;
int result = 0;
if (int.TryParse(txtMLPNumberHiddenLayer.Text, out result))
{
p.NumberHiddenLayer = result;
}
if (int.TryParse(txtMLPWindowSize.Text, out result))
{
p.WindowSize = result;
}
if (int.TryParse(txtMLPMaxEpoch.Text, out result))
{
p.MaxEpoch = result;
}
double dresult = 0;
if (double.TryParse(txtMLPMaxError.Text, out dresult))
{
p.MaxError = dresult;
}
p.Method = (MLPPredictor.TrainingMethod)cboMLPTrainingMethod.SelectedItem;
return(mMLP);
}
else if (selected_index == 2)
{
BasicNetworkPredictor p = BasicNetwork;
int result = 0;
if (int.TryParse(txtBasicNetworkNumberHiddenLayer.Text, out result))
{
p.NumberHiddenLayer = result;
}
if (int.TryParse(txtBasicNetworkWindowSize.Text, out result))
{
p.WindowSize = result;
}
if (int.TryParse(txtBasicNetworkMaxEpoch.Text, out result))
{
p.MaxEpoch = result;
}
double dresult = 0;
if (double.TryParse(txtBasicNetworkMaxError.Text, out dresult))
{
p.MaxError = dresult;
}
if (double.TryParse(txtBasicNetworkNormalizedHigh.Text, out dresult))
{
p.NormalizedHigh = dresult;
}
if (double.TryParse(txtBasicNetworkNormalizedLow.Text, out dresult))
{
p.NormalizedLow = dresult;
}
p.Method = (BasicNetworkPredictor.TrainingMethod)cboBasicNetworkTrainingMethod.SelectedItem;
return(mBasicNetwork);
}
else if (selected_index == 3)
{
SVMPredictor p = SVM;
int result = 0;
if (int.TryParse(txtSVMWindowSize.Text, out result))
{
p.WindowSize = result;
}
double dresult = 0;
if (double.TryParse(txtSVMNormalizedHigh.Text, out dresult))
{
p.NormalizedHigh = dresult;
}
if (double.TryParse(txtSVMNormalizedLow.Text, out dresult))
{
p.NormalizedLow = dresult;
}
return(mSVM);
}
else if (selected_index == 4)
{
RBFNetworkPredictor p = RBFNetwork;
int result = 0;
if (int.TryParse(txtRBFNetworkWindowSize.Text, out result))
{
p.WindowSize = result;
}
double dresult = 0;
if (double.TryParse(txtRBFNetworkNormalizedHigh.Text, out dresult))
{
p.NormalizedHigh = dresult;
}
if (double.TryParse(txtRBFNetworkNormalizedLow.Text, out dresult))
{
p.NormalizedLow = dresult;
}
return(mRBFNetwork);
}
else if (selected_index == 5)
{
GaussianDistributionPredictor p = GaussianDistribution;
p.PositveValueOnly = chkGaussinDistributionPositiveValueOnly.Checked;
return(mGaussianDistribution);
}
else if (selected_index == 6)
{
GeneticProgrammingPredictor p = GeneticProgramming;
int result = 0;
if (int.TryParse(txtGeneticProgrammingWindowSize.Text, out result))
{
p.WindowSize = result;
}
if (int.TryParse(txtGeneticProgrammingPopSize.Text, out result))
{
p.PopSize = result;
}
if (int.TryParse(txtGeneticProgrammingMaxEpoch.Text, out result))
{
p.MaxEpoch = result;
}
return(mGeneticProgrammingPredictor);
}
else if (selected_index == 7)
{
GaussianProcessPredictor p = GaussianProcess;
double result = 0;
if (double.TryParse(txtGaussianProcessSigma0.Text, out result))
{
p.Sigma0 = result;
}
if (double.TryParse(txtGaussianProcessLambda.Text, out result))
{
p.Lambda = result;
}
if (double.TryParse(txtGaussianProcessSigmaN.Text, out result))
{
p.SigmaN = result;
}
return(mGaussianProcess);
}
else
{
return(null);
}
}
public UcPredictorView()
{
InitializeComponent();
if (DesignMode)
{
return;
}
ARMAPredictor p0 = ARMA;
txtAROrder_ARMA.Text = string.Format("{0}", p0.AROrder);
txtMAOrder_ARMA.Text = string.Format("{0}", p0.MAOrder);
txtOptIter_ARMA.Text = string.Format("{0}", p0.OptIters);
txtLDSIter_ARMA.Text = string.Format("{0}", p0.LDSIters);
cboMLPTrainingMethod.DataSource = Enum.GetValues(typeof(MLPPredictor.TrainingMethod));
MLPPredictor p1 = MLP;
txtMLPNumberHiddenLayer.Text = string.Format("{0}", p1.NumberHiddenLayer);
txtMLPWindowSize.Text = string.Format("{0}", p1.WindowSize);
txtMLPMaxEpoch.Text = p1.MaxEpoch.ToString();
txtMLPMaxError.Text = p1.MaxError.ToString();
cboMLPTrainingMethod.SelectedItem = p1.Method;
cboBasicNetworkTrainingMethod.DataSource = Enum.GetValues(typeof(BasicNetworkPredictor.TrainingMethod));
BasicNetworkPredictor p2 = BasicNetwork;
txtBasicNetworkNumberHiddenLayer.Text = p2.NumberHiddenLayer.ToString();
txtBasicNetworkWindowSize.Text = string.Format("{0}", p2.WindowSize);
txtBasicNetworkMaxEpoch.Text = p2.MaxEpoch.ToString();
txtBasicNetworkMaxError.Text = p2.MaxError.ToString();
txtBasicNetworkNormalizedHigh.Text = p2.NormalizedHigh.ToString();
txtBasicNetworkNormalizedLow.Text = p2.NormalizedLow.ToString();
cboBasicNetworkTrainingMethod.SelectedItem = p2.Method;
SVMPredictor p3 = SVM;
txtSVMWindowSize.Text = string.Format("{0}", p3.WindowSize);
txtSVMNormalizedHigh.Text = p3.NormalizedHigh.ToString();
txtSVMNormalizedLow.Text = p3.NormalizedLow.ToString();
RBFNetworkPredictor p4 = RBFNetwork;
txtRBFNetworkWindowSize.Text = string.Format("{0}", p4.WindowSize);
txtRBFNetworkNormalizedHigh.Text = p4.NormalizedHigh.ToString();
txtRBFNetworkNormalizedLow.Text = p4.NormalizedLow.ToString();
GaussianDistributionPredictor p5 = GaussianDistribution;
chkGaussinDistributionPositiveValueOnly.Checked = p5.PositveValueOnly;
GeneticProgrammingPredictor p6 = GeneticProgramming;
txtGeneticProgrammingPopSize.Text = p6.PopSize.ToString();
txtGeneticProgrammingWindowSize.Text = p6.WindowSize.ToString();
txtGeneticProgrammingMaxEpoch.Text = p6.MaxEpoch.ToString();
GaussianProcessPredictor p7 = GaussianProcess;
txtGaussianProcessLambda.Text = p7.Lambda.ToString();
txtGaussianProcessSigma0.Text = p7.Sigma0.ToString();
txtGaussianProcessSigmaN.Text = p7.SigmaN.ToString();
}
