/// <summary>
/// Event handler startButton_Click
/// </summary>
private void startButton_Click(object sender, EventArgs e)
{
if (this.startButton.Text == "Start")
{
// get input neuron
try { this.inputLayerNeurons = Math.Max(1, Math.Min(48, Math.Min(this.variable.Observations / 2, int.Parse(this.inputLayerBox.Text)))); }
catch { this.inputLayerNeurons = Math.Min(this.variable.Observations / 2, 12); }
if (this.variable.SeriesValuesNoNaN.Count - this.inputLayerNeurons < 8)
{
MessageBox.Show("Unsufficent number of observations\nModel cannot be estimated with these variable",
"Neural Network Analysis", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
else if (this.variable.SeriesValuesNoNaN.Count - this.inputLayerNeurons < 28)
{
MessageBox.Show("Too few number of observations\nModel may be not adequate",
"Neural Network Analysis", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
}
// get hidden neuron
try { this.hiddenLayerNeurons = Math.Max(1, Math.Min(48, int.Parse(this.hiddenLayerBox.Text))); }
catch { this.hiddenLayerNeurons = 12; }
// get learning rate
try { this.learningRate = Math.Max(0.00001, Math.Min(1, double.Parse(this.learningRateBox.Text))); }
catch { this.learningRate = 0.05; }
// get momentum
try { this.momentum = Math.Max(0.00, Math.Min(1, double.Parse(this.momentumBox.Text))); }
catch { this.momentum = 0.5; }
// get max iteration
try { this.maxIteration = Math.Max(10, Math.Min(1000000, int.Parse(this.maxIterationBox.Text))); }
catch { this.maxIteration = 10000; }
//get activationfunction
if (this.semiLinearRadio.Checked == true) this.selectedActivationFunction = ActivationFunctionEnumeration.SemiLinearFunction;
else if (this.sigmoidRadio.Checked == true) this.selectedActivationFunction = ActivationFunctionEnumeration.SigmoidFunction;
else if (this.bipolarSigmoidRadio.Checked == true) this.selectedActivationFunction = ActivationFunctionEnumeration.BipolarSigmoidFunction;
else if (this.hyperbolicTangentRadio.Checked == true) this.selectedActivationFunction = ActivationFunctionEnumeration.HyperbolicTangentFunction;
//get with checking cycle
this.withCheckingCycle = this.withCheckingCycleCheck.Checked;
//get checking cycle
try { this.checkingCycle = Math.Max(1, Math.Min(10000, int.Parse(this.checkingCycleBox.Text))); }
catch { this.checkingCycle = 100; }
//get checking method
if (this.byMSEValueRadio.Checked == true) this.checkingMethod = CheckingMethodEnumeration.byMSEValue;
else if (this.byMSEChangeRadio.Checked == true) this.checkingMethod = CheckingMethodEnumeration.byMSEChange;
//get bymsevalue
try { this.byMSEValueStopping = Math.Max(0.000001, double.Parse(this.byMSEValueBox.Text)); }
catch { this.byMSEValueStopping = 0.1; }
//get bymsechange
try { this.byMSEChangeStopping = Math.Max(0.0000001, double.Parse(this.byMSEChangeBox.Text)); }
catch { this.byMSEChangeStopping = 0.01; }
//get use advance early stopping
this.useAdvanceEarlyStopping = this.useAdvanceCheck.Checked;
//get advance early stopping method
if (this.generalizationLossRadio.Checked == true) this.advanceStoppingMethod = AdvanceStoppingMethodEnumeration.GeneralizationLoss;
else if (this.PQRadio.Checked == true) this.advanceStoppingMethod = AdvanceStoppingMethodEnumeration.ProgressQuotient;
// get validation set ratio
try { this.validationSetRatio = Math.Max(0.0, Math.Min(0.5, double.Parse(this.validationSetRatioBox.Text))); }
catch { this.validationSetRatio = 0.3; }
// get generalization loss treshold
try { this.generalizationLossTreshold = Math.Max(0.0, Math.Min(90.0, double.Parse(this.generalizationLossTresholdBox.Text))); }
catch { this.generalizationLossTreshold = 2.0; }
// get PQ treshold
try { this.pqTreshold = Math.Max(0.0, Math.Min(90.0, double.Parse(this.PQTresholdBox.Text))); }
catch { this.pqTreshold = 1.5; }
//get strip
try { this.strip = Math.Max(2, Math.Min(20, int.Parse(this.stripBox.Text))); }
catch { this.strip = 5; }
this.updateSettings();
this.enableControls(false);
this.setProgressView();
needToStop = false;
this.learningThread = new Thread(new ThreadStart(this.searchSolution));
this.learningThread.Start();
}
else
{
// check if worker thread is running
if ((learningThread != null) && (learningThread.IsAlive))
{
// stop worker thread
needToStop = true;
learningThread.Join();
learningThread = null;
}
this.enableControls(true);
}
}
/// <summary>
/// Event handler startButton_Click
/// </summary>
private void startButton_Click(object sender, EventArgs e)
{
if (this.startButton.Text == "Start")
{
// get input neuron
try { this.inputLayerNeurons = Math.Max(1, Math.Min(48, Math.Min(this.variable.Observations / 2, int.Parse(this.inputLayerBox.Text)))); }
catch { this.inputLayerNeurons = Math.Min(this.variable.Observations / 2, 12); }
if (this.variable.SeriesValuesNoNaN.Count - this.inputLayerNeurons < 8)
{
MessageBox.Show("Unsufficent number of observations\nModel cannot be estimated with these variable",
"Neural Network Analysis", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
else if (this.variable.SeriesValuesNoNaN.Count - this.inputLayerNeurons < 28)
{
MessageBox.Show("Too few number of observations\nModel may be not adequate",
"Neural Network Analysis", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
}
// get hidden neuron
try { this.hiddenLayerNeurons = Math.Max(1, Math.Min(48, int.Parse(this.hiddenLayerBox.Text))); }
catch { this.hiddenLayerNeurons = 12; }
// get learning rate
try { this.learningRate = Math.Max(0.00001, Math.Min(1, double.Parse(this.learningRateBox.Text))); }
catch { this.learningRate = 0.05; }
// get momentum
try { this.momentum = Math.Max(0.00, Math.Min(1, double.Parse(this.momentumBox.Text))); }
catch { this.momentum = 0.5; }
// get max iteration
try { this.maxIteration = Math.Max(10, Math.Min(1000000, int.Parse(this.maxIterationBox.Text))); }
catch { this.maxIteration = 10000; }
//get activationfunction
if (this.semiLinearRadio.Checked == true)
{
this.selectedActivationFunction = ActivationFunctionEnumeration.SemiLinearFunction;
}
else if (this.sigmoidRadio.Checked == true)
{
this.selectedActivationFunction = ActivationFunctionEnumeration.SigmoidFunction;
}
else if (this.bipolarSigmoidRadio.Checked == true)
{
this.selectedActivationFunction = ActivationFunctionEnumeration.BipolarSigmoidFunction;
}
else if (this.hyperbolicTangentRadio.Checked == true)
{
this.selectedActivationFunction = ActivationFunctionEnumeration.HyperbolicTangentFunction;
}
//get with checking cycle
this.withCheckingCycle = this.withCheckingCycleCheck.Checked;
//get checking cycle
try { this.checkingCycle = Math.Max(1, Math.Min(10000, int.Parse(this.checkingCycleBox.Text))); }
catch { this.checkingCycle = 100; }
//get checking method
if (this.byMSEValueRadio.Checked == true)
{
this.checkingMethod = CheckingMethodEnumeration.byMSEValue;
}
else if (this.byMSEChangeRadio.Checked == true)
{
this.checkingMethod = CheckingMethodEnumeration.byMSEChange;
}
//get bymsevalue
try { this.byMSEValueStopping = Math.Max(0.000001, double.Parse(this.byMSEValueBox.Text)); }
catch { this.byMSEValueStopping = 0.1; }
//get bymsechange
try { this.byMSEChangeStopping = Math.Max(0.0000001, double.Parse(this.byMSEChangeBox.Text)); }
catch { this.byMSEChangeStopping = 0.01; }
//get use advance early stopping
this.useAdvanceEarlyStopping = this.useAdvanceCheck.Checked;
//get advance early stopping method
if (this.generalizationLossRadio.Checked == true)
{
this.advanceStoppingMethod = AdvanceStoppingMethodEnumeration.GeneralizationLoss;
}
else if (this.PQRadio.Checked == true)
{
this.advanceStoppingMethod = AdvanceStoppingMethodEnumeration.ProgressQuotient;
}
// get validation set ratio
try { this.validationSetRatio = Math.Max(0.0, Math.Min(0.5, double.Parse(this.validationSetRatioBox.Text))); }
catch { this.validationSetRatio = 0.3; }
// get generalization loss treshold
try { this.generalizationLossTreshold = Math.Max(0.0, Math.Min(90.0, double.Parse(this.generalizationLossTresholdBox.Text))); }
catch { this.generalizationLossTreshold = 2.0; }
// get PQ treshold
try { this.pqTreshold = Math.Max(0.0, Math.Min(90.0, double.Parse(this.PQTresholdBox.Text))); }
catch { this.pqTreshold = 1.5; }
//get strip
try { this.strip = Math.Max(2, Math.Min(20, int.Parse(this.stripBox.Text))); }
catch { this.strip = 5; }
this.updateSettings();
this.enableControls(false);
needToStop = false;
this.learningThread = new Thread(new ThreadStart(this.searchSolution));
this.learningThread.Start();
}
else
{
// check if worker thread is running
if ((learningThread != null) && (learningThread.IsAlive))
{
// stop worker thread
needToStop = true;
learningThread.Join();
learningThread = null;
}
this.enableControls(true);
}
}
