/// <summary>
/// Compute the output from this synapse.
/// </summary>
///
/// <param name="input">The input to this synapse.</param>
/// <returns>The output from this synapse.</returns>
public virtual IMLData Compute(IMLData input)
{
IMLData result = new BasicMLData(_outputCount);
if (_neurons.Count == 0)
{
throw new NeuralNetworkError(
"This network has not been evolved yet, it has no neurons in the NEAT synapse.");
}
int flushCount = 1;
if (_snapshot)
{
flushCount = _networkDepth;
}
// iterate through the network FlushCount times
for (int i = 0; i < flushCount; ++i)
{
int outputIndex = 0;
int index = 0;
result.Clear();
// populate the input neurons
while (_neurons[index].NeuronType == NEATNeuronType.Input)
{
_neurons[index].Output = input[index];
index++;
}
// set the bias neuron
_neurons[index++].Output = 1;
while (index < _neurons.Count)
{
NEATNeuron currentNeuron = _neurons[index];
double sum = 0;
foreach (NEATLink link in currentNeuron.InboundLinks)
{
double weight = link.Weight;
double neuronOutput = link.FromNeuron.Output;
sum += weight * neuronOutput;
}
var d = new double[1];
d[0] = sum / currentNeuron.ActivationResponse;
_activationFunction.ActivationFunction(d, 0, d.Length);
_neurons[index].Output = d[0];
if (currentNeuron.NeuronType == NEATNeuronType.Output)
{
result[outputIndex++] = currentNeuron.Output;
}
index++;
}
}
_outputActivationFunction.ActivationFunction(result.Data, 0,
result.Count);
return(result);
}
/// <inheritdoc/>
public IMLMethod Decode(NEATPopulation pop, Substrate.Substrate substrate,
IGenome genome)
{
// obtain the CPPN
NEATCODEC neatCodec = new NEATCODEC();
NEATNetwork cppn = (NEATNetwork)neatCodec.Decode(genome);
List <NEATLink> linkList = new List <NEATLink>();
IActivationFunction[] afs = new IActivationFunction[substrate.NodeCount];
IActivationFunction af = new ActivationSteepenedSigmoid();
// all activation functions are the same
for (int i = 0; i < afs.Length; i++)
{
afs[i] = af;
}
double c = this.MaxWeight / (1.0 - this.MinWeight);
BasicMLData input = new BasicMLData(cppn.InputCount);
// First create all of the non-bias links.
foreach (SubstrateLink link in substrate.Links)
{
SubstrateNode source = link.Source;
SubstrateNode target = link.Target;
int index = 0;
foreach (double d in source.Location)
{
input.Data[index++] = d;
}
foreach (double d in target.Location)
{
input.Data[index++] = d;
}
IMLData output = cppn.Compute(input);
double weight = output[0];
if (Math.Abs(weight) > this.MinWeight)
{
weight = (Math.Abs(weight) - this.MinWeight) * c
* Math.Sign(weight);
linkList.Add(new NEATLink(source.ID, target.ID,
weight));
}
}
// now create biased links
input.Clear();
int d2 = substrate.Dimensions;
IList <SubstrateNode> biasedNodes = substrate.GetBiasedNodes();
foreach (SubstrateNode target in biasedNodes)
{
for (int i = 0; i < d2; i++)
{
input.Data[d2 + i] = target.Location[i];
}
IMLData output = cppn.Compute(input);
double biasWeight = output[1];
if (Math.Abs(biasWeight) > this.MinWeight)
{
biasWeight = (Math.Abs(biasWeight) - this.MinWeight) * c
* Math.Sign(biasWeight);
linkList.Add(new NEATLink(0, target.ID, biasWeight));
}
}
// check for invalid neural network
if (linkList.Count == 0)
{
return(null);
}
linkList.Sort();
NEATNetwork network = new NEATNetwork(substrate.InputCount,
substrate.OutputCount, linkList, afs);
network.ActivationCycles = substrate.ActivationCycles;
return(network);
}
