///// <summary>
///// Funkcja y z definicji uzywana do modyfikacji wag neuronow radialnych
///// </summary>
///// <param name="fi"></param>
///// <returns></returns>
//private double y(double[] xk)
//{
// double result = 0;
// foreach( RadialNeuron neu in neuronHiddenLayer)
// {
// result += neu.fi(neu.u2(xk));
// }
// return result;
//}
/// <summary>
/// Gradient funkcji gaussa wzgledem c
/// </summary>
/// <param name="x"></param>
/// <returns></returns>
private double[] dE_dc(int nth_Output, int nth_Neuron)
{
double[] result = new double[documentList[0].GetValues().Count];
RadialNeuron radialNeuron = (RadialNeuron)neuronHiddenLayer[nth_Neuron];
LinearNeuron linerarNeuron = (LinearNeuron)neuronOutputLayer[nth_Output];
double[] inputVector;
double exp;
//double maxValue;
for (int wsp = 0; wsp < result.Length; wsp++)
{
for (int i = 0; i < documentList.Count; i++) //learningData.DataVectors.Count
{
inputVector = documentList[i].GetValues().ToArray(); // learningData.InputVectors[i];
exp = Math.Pow(Math.E, -radialNeuron.u2(inputVector) / 2);
result[wsp] += (y(linerarNeuron.Weights)[i] -
(outputData[nth_Output])[i]) * linerarNeuron.Weights[nth_Neuron] *
exp *radialNeuron.u(inputVector, wsp);
// Console.WriteLine(y(linerarNeuron.Weights)[i] + " - " +
// (learningData.OutputVectors[nth_Output])[i]+ " = " + (y(linerarNeuron.Weights)[i] -
// (learningData.OutputVectors[nth_Output])[i]));
}
}
return(result);
}
//dodałem TOMEK
public static void TestLoad(RadialNetwork rn)
{
Console.Out.WriteLine();
RadialNeuron.TestLoad((RadialNeuron)rn.neuronOutputLayer[0]);
Console.Out.WriteLine(rn.outputLayerNeutonWeights[0][0]);
Console.Out.WriteLine(rn.outputData[0][0]);
Console.Out.WriteLine(rn.neuronHiddenLayer.Count);
Console.Out.WriteLine(rn.neuronOutputLayer.Count);
//Console.Out.WriteLine(rn.greenMatrix[0, 0]);
}
/// <summary>
/// Konstruktor neuronu o okreslonej strukturze
/// </summary>
/// <param name="hiddenLayerInitNeuronCount">liczba neuronow ukrytych</param>
/// <param name="outputLayerNeuronCount">liczba neuronow wyjsciowych</param>
public RadialNetwork(int hiddenLayerInitNeuronCount, int outputLayerNeuronCount)
{
neuronHiddenLayer = new Collection <INeuron>();
neuronOutputLayer = new Collection <INeuron>();
outputLayerNeutonWeights = new List <double[]>();
for (int i = 0; i < hiddenLayerInitNeuronCount; i++)
{
INeuron r = new RadialNeuron();
neuronHiddenLayer.Add(r);
}
for (int i = 0; i < outputLayerNeuronCount; i++)
{
INeuron r = new LinearNeuron();
neuronOutputLayer.Add(r);
}
}
//dodałem Tomek
public static RadialNeuron TestSave()
{
RadialNeuron rn = new RadialNeuron();
rn.cellCenter = new double[1] {
1.0
};
rn.VectorSize = 1;
rn.sigma = new double[1] {
1.0
};
Console.Out.WriteLine(" " + rn.CellCenter[0]);
Console.Out.WriteLine(" " + rn.VectorSize);
Console.Out.WriteLine(" " + rn.sigma[0]);
return(rn);
}
//druga proba
//private double[] NetworkOutputVector(double[] vector)
//{
// return Matrix.Multiply(this.CreateGreenMatrix(), vector);
//}
#endregion
//dodałem TOMEK
public static RadialNetwork TestSave()
{
RadialNetwork rn = new RadialNetwork();
rn.outputLayerNeutonWeights.Add(new double[] { 1.0 });
rn.outputData = new List <double[]>();
rn.outputData.Add(new double[] { 1.0 });
//rn.greenMatrix = new double[1, 1];
//rn.greenMatrix[0, 0] = 2;
rn.neuronOutputLayer[0] = (INeuron)RadialNeuron.TestSave();
Console.Out.WriteLine(rn.outputLayerNeutonWeights[0][0]);
Console.Out.WriteLine(rn.outputData[0][0]);
Console.Out.WriteLine(rn.neuronHiddenLayer.Count);
Console.Out.WriteLine(rn.neuronOutputLayer.Count);
//Console.Out.WriteLine(rn.greenMatrix[0, 0]);
return(rn);
}
//dodałem Tomek
public static void TestLoad(RadialNeuron rn)
{
Console.Out.WriteLine(" " + rn.CellCenter[0]);
Console.Out.WriteLine(" " + rn.VectorSize);
Console.Out.WriteLine(" " + rn.sigma[0]);
}
