/*
* Calcula distância euclidiana do padrão de entrada
*/
public double GetEuclideanDistance(DataSetObject pattern)
{
double dist = 0;
if (pattern.GetInputLenght() != weights.Length)
{
throw new Exception("Incorrect data format!");
}
else
{
double[] inputWeights = pattern.GetInput();
if (weights.Length != inputWeights.Length)
{
throw new Exception("Incorrect data format!");
}
else
{
for (int x = 0; x < weights.Length; x++)
{
dist += ((inputWeights[x] - weights[x]) * (inputWeights[x] - weights[x]));
}
}
}
return(dist);
}
/*
* Dado um padrão de entrada e uma variável de aprendizado, atualiza os pesos do neurônio
*/
public void UpdateWeights(DataSetObject pattern, double learningRate)
{
if (pattern.GetInputLenght() != weights.Length)
{
throw new Exception("Incorrect data format!");
}
else
{
double[] inputWeights = pattern.GetInput();
for (int i = 0; i < inputWeights.Length; i++)
{
weights[i] += learningRate * (inputWeights[i] - weights[i]);
}
}
}
/*
* Método de aprendizado da Rede
*/
public void Learn(DataSet trainingSet)
{
if (trainingSet.GetOutputSize() != this.outputLayerSize || trainingSet.GetInputSize() != this.inputLayerSize)
{
throw new Exception("Incorrect data format!");
}
else
{
bool learned = false;
List <DataSetObject> patterns = trainingSet.GetList();
for (int it = 1; learned == false && it <= maxIterationNumber; it++)
{
//VAI APRESENTANDO OS PADRÕES, DE FORMA ALTERNADA, ATÉ QUE TODOS ATINJAM O ERRO REQUERIDO.
int allPatternOK = 0;
for (int p = 0; p < patterns.Count; p++)
{
DataSetObject pattern = patterns[p];
//INJETA O PADRÃO NA ENTRADA DA REDE.
ApplyPattern(pattern.GetInput());
//PROPAGA
DoPropagation();
if (CalculateStopError(pattern.GetTargetOutput()) > this.Error)
{
DoBackPropagation(pattern.GetTargetOutput());
}
else
{
allPatternOK++;
}
if (allPatternOK == patterns.Count)
{
learned = true;
}
iterationNumber = it;
}
}
}
}
