private void Backpropagate(RadialBasisFunctionLayer outputRadialBasisFunctionLayer,
RadialBasisFunctionLayer hiddenRadialBasisFunctionLayer, double outputValue, int layer)
{
double[,] weightMatrix = null;
foreach (var matrix in weightMatrixes)
{
if (matrix.Item2 == outputRadialBasisFunctionLayer.Layer)
{
weightMatrix = matrix.Item3;
break;
}
}
for (int i = 0; i < outputRadialBasisFunctionLayer.Values.Count(); i++)
{
for (int j = 0; j < hiddenRadialBasisFunctionLayer.Values.Count(); j++)
{
var tk = (double)outputValue;
var ok = outputRadialBasisFunctionLayer.Values[i];
var oj = hiddenRadialBasisFunctionLayer.Values[j];
var sigmak = ok * (1 - ok) * (tk - ok);
var deltaWk = LearningRate * sigmak * oj;
weightMatrix[j, i] += deltaWk;
}
}
}
private void ClassifyOutputLayer(RadialBasisFunctionLayer hiddenRadialBasisFunctionLayer, RadialBasisFunctionLayer outputRadialBasisFunctionLayer)
{
double[,] weightMatrix = null;
foreach (var matrix in weightMatrixes)
{
if (matrix.Item1 == hiddenRadialBasisFunctionLayer.Layer - 1)
{
weightMatrix = matrix.Item3;
}
}
for (int i = 0; i < outputRadialBasisFunctionLayer.Values.Count(); i++)
{
double summe = 0.0;
for (int j = 0; j < hiddenRadialBasisFunctionLayer.Values.Count(); j++)
{
summe += hiddenRadialBasisFunctionLayer.Values[j] * weightMatrix[j, i]; //j, i];
}
outputRadialBasisFunctionLayer.Values[i] = outputRadialBasisFunctionLayer.ActivationFunction_.ActivationFunction(summe);
}
}
private void ClassifyHiddenLayer(RadialBasisFunctionLayer inputRadialBasisFunctionLayer, RadialBasisFunctionLayer hiddenRadialBasisFunctionLayer)
{
for (int i = 0; i < hiddenRadialBasisFunctionLayer.Values.Count(); i++)
{
var sigma = 0.0;
var sigmaSumme = 0.0;
var expression = 0.0;
for (int j = 0; j < inputRadialBasisFunctionLayer.Values.Count(); j++)
{
var xj = inputRadialBasisFunctionLayer.Values[j];
var cj = 0.0;
if (hiddenRadialBasisFunctionLayer.Cetroids[i] != null)
{
cj = hiddenRadialBasisFunctionLayer.Cetroids[i][j];
}
sigmaSumme += (xj - cj) * (xj - cj);
}
var smallSigma = hiddenRadialBasisFunctionLayer.Sigmoids[i];
expression = -1 * sigmaSumme / 2 * smallSigma * smallSigma;
sigma = Math.Pow(Math.E, expression);
hiddenRadialBasisFunctionLayer.Values[i] = sigma;
}
}
public void BuildWeightMatrixOutputLayer(RadialBasisFunctionLayer radialBasisFunctionLayer)
{
weightMatrixes.Add(new Tuple <int, int, double[, ]>(
1, 2, new double[radialBasisFunctionLayer.Neurons, radialBasisFunctionLayer.Neurons]));
}
public void AddOutputLayer(RadialBasisFunctionLayer radialBasisFunctionLayer)
{
outputRadialBasisFunctionLayer = radialBasisFunctionLayer;
BuildWeightMatrixOutputLayer(radialBasisFunctionLayer);
}
public void AddHiddenLayer(RadialBasisFunctionLayer radialBasisFunctionLayer)
{
hiddenRadialBasisFunctionLayer = radialBasisFunctionLayer;
BuildWeightMatrix(radialBasisFunctionLayer);
}
public void AddInputLayer(RadialBasisFunctionLayer radialBasisFunctionLayer)
{
inputRadialBasisFunctionLayer = radialBasisFunctionLayer;
}
