/// <summary>
/// Обучить нейрон-победитель.
/// </summary>
/// <param name="neuronWinner">Нейрон-победитель.</param>
/// <param name="inputEntity">Входной вектор.</param>
/// <param name="learningRate">Скорость обучения.</param>
public void StudyNeuron(KohonenNeuron neuronWinner, NetworkDataEntity inputEntity, double learningRate)
{
for (int i = 0; i < neuronWinner.Weights.Count(); i++)
{
neuronWinner.Weights[i] = neuronWinner.Weights[i] +
learningRate * (inputEntity.AttributeValues[i].GetNormalizedValue(NormalizationType) - neuronWinner.Weights[i]);
}
}
private void CreateGraphFromANN()
{
BidirectionalGraph <object, IEdge <object> > g = new BidirectionalGraph <object, IEdge <object> >();
if (RNA is Kohonen)
{
int inputN = ((Kohonen)RNA).GetInputLayerSize();
for (int x = 0; x < inputN; x++)
{
g.AddVertex("Input-" + (x + 1).ToString());
}
KohonenNeuron[,] layer = ((Kohonen)RNA).GetCompetitiveLayer();
for (int i = 0; i < ((Kohonen)RNA).GetCompetitiveLayerLength(); i++)
{
for (int j = 0; j < ((Kohonen)RNA).GetCompetitiveLayerLength(); j++)
{
KohonenNeuron neuron = layer[i, j];
g.AddVertex(neuron);
for (int x2 = 0; x2 < inputN; x2++)
{
g.AddEdge(new Edge <object>("Input-" + (x2 + 1).ToString(), neuron));
}
}
}
}
else //is Backpropagation
{
List <BackpropagationLayer> layers = ((Backpropagation)RNA).GetLayers();
foreach (BackpropagationLayer layer in layers)
{
foreach (BackpropagationNeuron neuron in layer.neurons)
{
g.AddVertex(neuron);
}
}
foreach (BackpropagationLayer layer in layers)
{
foreach (BackpropagationNeuron neuron in layer.neurons)
{
foreach (BackpropagationConnection conn in neuron.listConnection)
{
g.AddEdge(new Edge <object>(conn.neuron, neuron));
}
}
}
}
graphLayout.Graph = g;
graphLayout.LayoutMode = LayoutMode.Automatic;
graphLayout.LayoutAlgorithmType = "Tree";
}
/// <summary>
/// Получить нейрон-победитель.
/// </summary>
/// <param name="inputEntity">Входной вектор.</param>
/// <returns>Нейрон-победитель для входного вектора.</returns>
public override KohonenNeuron GetNeuronWinner(NetworkDataEntity inputEntity)
{
var attributeValues = inputEntity.AttributeValues.Select(v => v.GetNormalizedValue(NormalizationType));
double minDistance = GetEuclideanDistance(Neurons[0].Weights, attributeValues);
KohonenNeuron neuronWinner = Neurons[0];
for (int i = 1; i < Neurons.Count; i++)
{
double currentDistance = GetEuclideanDistance(Neurons[i].Weights, attributeValues);
if (currentDistance < minDistance)
{
minDistance = currentDistance;
neuronWinner = Neurons[i];
}
}
return(neuronWinner);
}
private void btnRecognize_Click(object sender, RoutedEventArgs e)
{
if (IsPatternRight())
{
if (RNA is Backpropagation)
{
double[] ret = RNA.Recognize(GetInputPattern());
DrawGraph();
txtResult.Text = "";
string linebreak = "";
string comma = "";
foreach (double r in ret)
{
txtResult.Text += comma + linebreak + r.ToString();
linebreak = "\n";
comma = ", ";
}
}
else // is Kohonen
{
KohonenNeuron neuron = ((Kohonen)RNA).RecognizeWinnerNeuron(GetInputPattern());
txtResult.Text = "Neurônio Vencedor: " + neuron.ToString() + "\n\n";
txtResult.Text += "Pesos:";
int x = 1;
foreach (double w in neuron.weights)
{
txtResult.Text += "\nInput-" + x.ToString() + ": " + w.ToString();
x++;
}
}
}
else
{
MessageBox.Show("Padrão de entrada com formato incorreto!");
txtInputPattern.Focus();
}
}
private void Click_Edge(object sender, RoutedEventArgs e)
{
if (RNA is Backpropagation)
{
BackpropagationNeuron source = (BackpropagationNeuron)((Edge <object>)(((EdgeControl)sender).Edge)).Source;
BackpropagationNeuron target = (BackpropagationNeuron)((Edge <object>)(((EdgeControl)sender).Edge)).Target;
foreach (BackpropagationConnection conn in target.listConnection)
{
if (conn.neuron == source)
{
graphText.Content = "Peso: " + conn.valueWeight;
break;
}
}
}
else // is Kohonen
{
string source = (string)((Edge <object>)(((EdgeControl)sender).Edge)).Source;
KohonenNeuron target = (KohonenNeuron)((Edge <object>)(((EdgeControl)sender).Edge)).Target;
string[] sA = source.Split('-');
int wIndex = (Convert.ToInt32(sA[1]) - 1);
graphText.Content = "Peso: " + target.weights[wIndex];
}
}