/// <summary>
/// Create a default minimal genome that describes a NN with the given number of inputs and outputs.
/// </summary>
/// <returns></returns>
public static IGenome CreateGenome(NeatParameters neatParameters, IdGenerator idGenerator, int inputNeuronCount, int outputNeuronCount, float connectionProportion)
{
IActivationFunction actFunct;
NeuronGene neuronGene; // temp variable.
NeuronGeneList inputNeuronGeneList = new NeuronGeneList(); // includes bias neuron.
NeuronGeneList outputNeuronGeneList = new NeuronGeneList();
NeuronGeneList neuronGeneList = new NeuronGeneList();
ConnectionGeneList connectionGeneList = new ConnectionGeneList();
// IMPORTANT NOTE: The neurons must all be created prior to any connections. That way all of the genomes
// will obtain the same innovation ID's for the bias,input and output nodes in the initial population.
// Create a single bias neuron.
//TODO: DAVID proper activation function change to NULL?
actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Bias, actFunct);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
// Create input neuron genes.
actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
for (int i = 0; i < inputNeuronCount; i++)
{
//TODO: DAVID proper activation function change to NULL?
neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Input, actFunct);
inputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Create output neuron genes.
//actFunct = ActivationFunctionFactory.GetActivationFunction("NullFn");
for (int i = 0; i < outputNeuronCount; i++)
{
//actFunct = ActivationFunctionFactory.GetActivationFunction("LinearNoAbs");
actFunct = ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid");
//actFunct = ActivationFunctionFactory.GetRandomActivationFunction(neatParameters);
//TODO: DAVID proper activation function
neuronGene = new NeuronGene(idGenerator.NextInnovationId, NeuronType.Output, actFunct);
outputNeuronGeneList.Add(neuronGene);
neuronGeneList.Add(neuronGene);
}
// Loop over all possible connections from input to output nodes and create a number of connections based upon
// connectionProportion.
foreach (NeuronGene targetNeuronGene in outputNeuronGeneList)
{
foreach (NeuronGene sourceNeuronGene in inputNeuronGeneList)
{
// Always generate an ID even if we aren't going to use it. This is necessary to ensure connections
// between the same neurons always have the same ID throughout the generated population.
uint connectionInnovationId = idGenerator.NextInnovationId;
if (Utilities.NextDouble() < connectionProportion)
{ // Ok lets create a connection.
connectionGeneList.Add(new ConnectionGene(connectionInnovationId,
sourceNeuronGene.InnovationId,
targetNeuronGene.InnovationId,
(Utilities.NextDouble() * neatParameters.connectionWeightRange) - neatParameters.connectionWeightRange / 2.0)); // Weight 0 +-5
}
}
}
// Don't create any hidden nodes at this point. Fundamental to the NEAT way is to start minimally!
return(new NeatGenome(idGenerator.NextGenomeId, neuronGeneList, connectionGeneList, inputNeuronCount, outputNeuronCount));
}