public void NotifyNeuronsWeightChange(int layerNo, int neuronNo, int inputNo, double newWeight)
{
Network.Topology.Layers[layerNo].Neurons[neuronNo].Inputs[inputNo].Weight = newWeight;
Network.PropagateValuesForward(null, layerNo);
RefreshDgvNeurons();
MainWindow.ucDialogDgvNeuronsShow.SetControls(dgvNeurons, (int)nudDigits.Value);
dgvNeurons.Focus();
TestNetwork();
}
private void BCreateNetwork_Click(object sender, EventArgs e)
{
// TODO: ERROR: Un-check "network uses bias" on crete and will be crash!
// TODO: ERROR: Something is wrong when new network is bigger (I don't think smaller) then current before creating new one.
if (nudMinWeight.Value > nudMaxWeight.Value)
{
MessageBox.Show(this, "Minimum weight is greater than maximum weight!", "Wrong minimum and maximum weight values", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
// TODO: EVOLUTION: validation seems to be neccesery! All rows should be fill correctly or should be information about not used rows
List <LayerCreationInfo> layerCreationInfoList = new List <LayerCreationInfo>();
for (int row = 0; row < dgvLayers.RowCount; row++)
{
if (dgvLayers.Rows[row].Cells["NeuronsQuantity"].Value == null ||
dgvLayers.Rows[row].Cells["Function"].Value == null)
{
continue;
}
String quantity = dgvLayers.Rows[row].Cells["NeuronsQuantity"].Value.ToString();
try
{
int intQuantity = Int32.Parse(quantity);
if (intQuantity < 1)
{
MessageBox.Show(this, quantity + " <- this value is a bed idea!", "Wrong value", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
layerCreationInfoList.Add(new LayerCreationInfo()
{
HowManyNeuronsPerLayer = intQuantity + (cbUsesBiasNeurons.Checked ? 1 : 0),
LayerNo = row,
LayerActivationFunction = ActivationFunctionFromRowIdx(row),
PreviousLayerNeuronsCount = layerCreationInfoList.Count == 0 ? 0 : layerCreationInfoList.ElementAt(layerCreationInfoList.Count - 1).HowManyNeuronsPerLayer
});
} catch (FormatException)
{
MessageBox.Show(this, quantity + " is not a decimal value!", "Wrong value", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
}
// last layer has addde redundant bias neuron, need decrease neurons count:
layerCreationInfoList.Last().HowManyNeuronsPerLayer--;
if (layerCreationInfoList.Count < 2)
{
MessageBox.Show(this, "Network must have at least two layers!", "Need more layers", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
RandomWeight.MinWeight = (double)nudMinWeight.Value;
RandomWeight.MaxWeight = (double)nudMaxWeight.Value;
Topology topology = new Topology(layerCreationInfoList, cbUsesBiasNeurons.Checked);
NeuralNetwork network = new NeuralNetwork(
topology,
(double)nudMinWeight.Value,
(double)nudMaxWeight.Value,
cbLearningMethod.SelectedIndex == 0 ? NeuralNetwork.LearningMethod.NOT_LINEAR : NeuralNetwork.LearningMethod.LINEAR,
tbName.Text
);
network.PropagateValuesForward();
MainWindow.NotifyNetworkCreated(network);
this.Visible = false;
}