internal void ApplyLearning(NeuralLayer neuralLayer, double learningRate)
{
foreach (var item in Input)
{
item.ApplyLearning(learningRate);
}
lastdelta = delta;
weight += delta * learningRate;
}
internal void InitiateLearning(NeuralLayer neuralLayer)
{
foreach (var item in Input)
{
item.LastDeltaWeight = 0;
item.DeltaWeight = 0;
}
delta = 0;
lastdelta = 0;
}
internal void Pulse(NeuralLayer neuralLayer)
{
Output = 0;
foreach (var item in Input)
{
Output += item.Input.Output * item.Weight;
}
Output += bias;
Output = ActivateFunction(Output);
}
public void Init(int seed, int inputNeurons, int hiddenNeurons, int outputNeurons)
{
inputLayer = new NeuralLayer();
hiddenLayer = new NeuralLayer();
outputLayer = new NeuralLayer();
fitness = 100;
this.seed = seed;
Random rand = new Random(this.seed);
Random rand2 = new Random(this.seed);
Random rand3 = new Random(this.seed);
Random rand4 = new Random(this.seed);
int i, j;
for (i = 0; i < inputNeurons; i++)
{
inputLayer.neurons.Add(new Neuron(0));
}
for (i = 0; i < hiddenNeurons; i++)
{
hiddenLayer.neurons.Add(new Neuron(rand.NextDouble()));
}
for (i = 0; i < outputNeurons; i++)
{
outputLayer.neurons.Add(new Neuron(rand2.NextDouble()));
}
//Wire input together with the hidden layer
for (i = 0; i < hiddenLayer.neurons.Count; i++)
{
for (j = 0; j < inputLayer.neurons.Count; j++)
{
hiddenLayer.neurons[i].Input.Add(new Link(inputLayer.neurons[j], rand3.NextDouble()));
}
}
//Wire output together with hidden layer
for (i = 0; i < outputLayer.neurons.Count; i++)
{
for (j = 0; j < hiddenLayer.neurons.Count; j++)
{
outputLayer.neurons[i].Input.Add(new Link(hiddenLayer.neurons[j], rand4.NextDouble()));
}
}
}
