public void Train(int inputIndex, double[] inputs)
{
for (int i = 0; i < numNeurons; i++)//neuron
{
inputs[i] = 0.0;
for (int j = 0; j < numLearnNeurons; j++)//weight
{
inputs[i] += inputData[inputIndex] * inputWeights[j, i];
}
inputs[i] = Sigmoid.Func(inputs[i]);
}
outputData[inputIndex] = 0.0;
for (int i = 0; i < numNeurons; i++)
{
outputData[inputIndex] += hiddenWeights[i] * inputs[i];
}
}
public void Compute(double x)
{
double outpt;
for (int i = 0; i < numNeurons; i++)//neuron
{
values[i] = 0.0;
for (int j = 0; j < numLearnNeurons; j++)//weight
{
values[i] += x * inputWeights[j, i];
}
values[i] = Sigmoid.Func(values[i]);
}
outpt = 0.0;
for (int i = 0; i < numNeurons; i++)
{
outpt += hiddenWeights[i] * values[i];
}
Console.WriteLine("input = " + x.ToString("0.0000") + " result = " + outpt + " error = " + Math.Abs(((Math.Sqrt(x) - outpt) / outpt) * 100).ToString("0.0000"));
}
public void AdjustWeights(int inputIndex, double[] inputs)
{
for (int i = 0; i < numNeurons; i++)
{
hiddenWeights[i] -= (outputData[inputIndex] - expectedOutput[inputIndex]) * inputs[i] * learnRate;
for (int j = 0; j < numLearnNeurons; j++)
{
inputWeights[j, i] -= (outputData[inputIndex] - expectedOutput[inputIndex]) * Sigmoid.Derivative(inputs[i]) * hiddenWeights[i] * outputData[inputIndex] * learnRate;
}
}
}
