public double[] Execute(double[] inputs)
{
if (inputs.Length != inputNeurons.Length)
{
throw new ArgumentOutOfRangeException("The number of inputs must match the number of input neurons.");
}
foreach (Neuron neuron in GetAllNeurons())
{
neuron.Value = 0;
}
for (int i = 0; i < inputs.Length; i++)
{
inputNeurons[i].Value = inputs[i];
}
List <int> layersToGoThrough = new List <int>();
layersToGoThrough.AddRange(GetHiddenLayers());
layersToGoThrough.Add((int)Neuron.IONeuronType.Output);
foreach (int layer in layersToGoThrough)
{
foreach (Neuron neuron in GetNeuronsForLayer(layer))
{
Synapse[] synapsesToNeuron = synapses.Where(x => x.ToLayer == layer && x.ToNeuron == neuron.NeuronPosition).ToArray();
double value = 0;
foreach (Synapse synapseFromneuron in synapsesToNeuron)
{
Neuron[] connectedNeurons = GetAllNeurons().Where(y => y.Layer == synapseFromneuron.FromLayer && y.NeuronPosition == synapseFromneuron.FromNeuron).ToArray();
foreach (Neuron connectedNeuron in connectedNeurons)
{
value += connectedNeuron.Value * synapseFromneuron.Weight;
}
}
neuron.Value = activationMethod.Invoke(value + neuron.Bias);
}
}
double[] outputs = new double[outputNeurons.Length];
for (int i = 0; i < outputNeurons.Length; i++)
{
outputs[i] = outputNeurons[i].Value;
}
return(outputs);
}
