/// <summary>
/// Trains the Neural Network by feeding forward, then back propgating
/// </summary>
public void Train(double[] inputs, double[] targetOutputs)
{
double[] actualOutputs = RunNetwork(inputs);
var outputNeuronLayer = Neurons.Length - 1;
var outputNeurons = Neurons[outputNeuronLayer];
var errorTotal = 0.0;
for (int n = 0; n < outputNeurons.Length; n++)
{
errorTotal += .5 * (targetOutputs[n] - actualOutputs[n]) * (targetOutputs[n] - actualOutputs[n]);
}
TotalError = errorTotal;
for (int l = Neurons.Length - 1; l > 0; l--)
{
var layerPrev = Neurons[l - 1];
var layer = Neurons[l];
for (int n = 0; n < layer.Length; n++)
{
var neuron = layer[n];
if (l == outputNeuronLayer)
{
neuron.Delta = -1 * (targetOutputs[n] - neuron.Output) * ActivatorFunction.ExecuteDerivative(neuron.Output);
}
else
{
var layerNext = Neurons[l + 1];
var wDeltaSum = 0.0;
for (int w = 0; w < layerNext.Length; w++)
{
wDeltaSum = layerNext[w].Delta;
}
var derivedPart = ActivatorFunction.ExecuteDerivative(neuron.Output);
neuron.Delta = -1 * derivedPart * wDeltaSum;
}
for (int w = 0; w < neuron.Weights.Length; w++)
{
var wPrime = neuron.Weights[w] - LearningRate * neuron.Delta * layerPrev[w].Output;
neuron.Weights[w] = wPrime;
}
neuron.BiasWeight = neuron.BiasWeight - LearningRate * neuron.Delta;
}
}
}