private void numNeurons_ValueChanged(object sender, EventArgs e)
{
int n = (int)numNeurons.Value;
if (_Layer.NumberOfNeurons != n)
{
_Layer.NumberOfNeurons = n;
panelNeurons.Controls.Clear();
Point p = new Point(50, 0);
for (int i = 0; i < _Layer.Neurons.Count; i++)
{
NeuronControl nc = new NeuronControl();
nc.Neuron = _Layer.Neurons[i];
nc.NeuronControlClicked = new NeuronControl.NeuronControlClickedDelegate(SetCurrentNeuron);
panelNeurons.Controls.Add(nc);
nc.Location = p;
p.Y += 55;
}
}
}
void SetCurrentNeuron(NeuronControl neuron_control)
{
if (!txtNeuronThreshold.Enabled)
{
txtNeuronThreshold.Enabled = true;
}
if (!btnRandThreshold.Enabled)
{
btnRandThreshold.Enabled = true;
}
foreach (NeuronControl nc in panelNeurons.Controls)
{
if (!nc.Equals(neuron_control))
{
nc.Selected = false;
}
}
neuron_control.Selected = true;
SelectedNeuron = neuron_control.Neuron;
txtNeuronThreshold.Value = (decimal)SelectedNeuron.Threshold;
}
void SetUI()
{
if (_Layer != null)
{
prenn = _Layer.NumberOfNeurons;
this.labLayerType.Text = "Layer type: " + _Layer.LayerType.ToString() + " layer";
numNeurons.Value = _Layer.NumberOfNeurons;
Point p = new Point(50, 0);
for (int i = 0; i < _Layer.Neurons.Count; i++)
{
NeuronControl nc = new NeuronControl();
nc.Neuron = _Layer.Neurons[i];
nc.NeuronControlClicked = new NeuronControl.NeuronControlClickedDelegate(SetCurrentNeuron);
panelNeurons.Controls.Add(nc);
nc.Location = p;
p.Y += 55;
}
switch (_Layer.InputCombinationFunction)
{
case InputCombinationFunctionType.LinearProduct:
radioCFLP.Checked = true;
labCFHelp.Text = "The net input is the sum of the linear products of outputs of the neurons in the previous layer and weights of the pre synapses.";
break;
case InputCombinationFunctionType.EucledianDistance:
radCFED.Checked = true;
labCFHelp.Text = "The net input is the sum of eucledian distance between the synapses weights and outputs of the previous layers neuron.";
break;
case InputCombinationFunctionType.ManhattanDistance:
radCFMD.Checked = true;
labCFHelp.Text = "The net input is the sum of manhattan distance between the synapses weights and outputs of the previous layers neuron.";
break;
}
if (_Layer.ActivationFunction != null)
{
switch (_Layer.ActivationFunction.FunctionName)
{
case "Unipolar Sign Function":
USF = (UnipolarSignFunction)_Layer.ActivationFunction;
radAFUSF.Checked = true;
break;
case "Bipolar Sign Function":
BSF = (BipolarSignFunction)_Layer.ActivationFunction;
radAFBSF.Checked = true;
break;
case "Unipolar Linear Function":
ULF = (UnipolarLinearFunction)_Layer.ActivationFunction;
radAFULF.Checked = true;
break;
case "Bipolar Linear Function":
BLF = (BipolarLinearFunction)_Layer.ActivationFunction;
radAFBLF.Checked = true;
break;
case "Sigmoidal Unipolar Function":
SUF = (SigmoidalUnipolarFunction)_Layer.ActivationFunction;
radAFSUF.Checked = true;
break;
case "Sigmoidal Bipolar Function":
SBF = (SigmoidalBipolarFunction)_Layer.ActivationFunction;
radAFSBF.Checked = true;
break;
case "Tangent Hyperbolic Function":
THF = (TangentHyperbolicFunction)_Layer.ActivationFunction;
radAFTHF.Checked = true;
break;
case "Gaussian Activation Function":
GAF = (GaussianActivationFunction)_Layer.ActivationFunction;
radAFGAF.Checked = true;
break;
}
SetAFParams();
}
else
{
radAFNone.Checked = true;
}
}
}
void SetCurrentNeuron(NeuronControl neuron_control)
{
if (!txtNeuronThreshold.Enabled) txtNeuronThreshold.Enabled = true;
if (!btnRandThreshold.Enabled) btnRandThreshold.Enabled = true;
foreach (NeuronControl nc in panelNeurons.Controls)
{
if (!nc.Equals(neuron_control))
{
nc.Selected = false;
}
}
neuron_control.Selected = true;
SelectedNeuron = neuron_control.Neuron;
txtNeuronThreshold.Value = (decimal)SelectedNeuron.Threshold;
}
private void numNeurons_ValueChanged(object sender, EventArgs e)
{
int n = (int)numNeurons.Value;
if (_Layer.NumberOfNeurons != n)
{
_Layer.NumberOfNeurons = n;
panelNeurons.Controls.Clear();
Point p = new Point(50, 0);
for (int i = 0; i < _Layer.Neurons.Count; i++)
{
NeuronControl nc = new NeuronControl();
nc.Neuron = _Layer.Neurons[i];
nc.NeuronControlClicked = new NeuronControl.NeuronControlClickedDelegate(SetCurrentNeuron);
panelNeurons.Controls.Add(nc);
nc.Location = p;
p.Y += 55;
}
}
}
void SetUI()
{
if (_Layer != null)
{
prenn = _Layer.NumberOfNeurons;
this.labLayerType.Text = "Layer type: " + _Layer.LayerType.ToString() + " layer";
numNeurons.Value = _Layer.NumberOfNeurons;
Point p = new Point(50, 0);
for (int i = 0; i < _Layer.Neurons.Count; i++)
{
NeuronControl nc = new NeuronControl();
nc.Neuron = _Layer.Neurons[i];
nc.NeuronControlClicked = new NeuronControl.NeuronControlClickedDelegate(SetCurrentNeuron);
panelNeurons.Controls.Add(nc);
nc.Location = p;
p.Y += 55;
}
switch (_Layer.InputCombinationFunction)
{
case InputCombinationFunctionType.LinearProduct:
radioCFLP.Checked = true;
labCFHelp.Text = "The net input is the sum of the linear products of outputs of the neurons in the previous layer and weights of the pre synapses.";
break;
case InputCombinationFunctionType.EucledianDistance:
radCFED.Checked = true;
labCFHelp.Text = "The net input is the sum of eucledian distance between the synapses weights and outputs of the previous layers neuron.";
break;
case InputCombinationFunctionType.ManhattanDistance:
radCFMD.Checked = true;
labCFHelp.Text = "The net input is the sum of manhattan distance between the synapses weights and outputs of the previous layers neuron.";
break;
}
if (_Layer.ActivationFunction != null)
{
switch (_Layer.ActivationFunction.FunctionName)
{
case "Unipolar Sign Function":
USF = (UnipolarSignFunction)_Layer.ActivationFunction;
radAFUSF.Checked = true;
break;
case "Bipolar Sign Function":
BSF = (BipolarSignFunction)_Layer.ActivationFunction;
radAFBSF.Checked = true;
break;
case "Unipolar Linear Function":
ULF = (UnipolarLinearFunction)_Layer.ActivationFunction;
radAFULF.Checked = true;
break;
case "Bipolar Linear Function":
BLF = (BipolarLinearFunction)_Layer.ActivationFunction;
radAFBLF.Checked = true;
break;
case "Sigmoidal Unipolar Function":
SUF = (SigmoidalUnipolarFunction)_Layer.ActivationFunction;
radAFSUF.Checked = true;
break;
case "Sigmoidal Bipolar Function":
SBF = (SigmoidalBipolarFunction)_Layer.ActivationFunction;
radAFSBF.Checked = true;
break;
case "Tangent Hyperbolic Function":
THF = (TangentHyperbolicFunction)_Layer.ActivationFunction;
radAFTHF.Checked = true;
break;
case "Gaussian Activation Function":
GAF = (GaussianActivationFunction)_Layer.ActivationFunction;
radAFGAF.Checked = true;
break;
}
SetAFParams();
}
else
{
radAFNone.Checked = true;
}
}
}
