public void TestNetworkSumOutputPort()
{
var multipliers = new Dictionary <int, IReceptiveField>();
multipliers[0] = new MulReceptiveField(0.0, 1.0);
multipliers[1] = new MulReceptiveField(1.0, 1.0);
multipliers[2] = new MulReceptiveField(2.0, 1.0);
multipliers[3] = new MulReceptiveField(3.0, 1.0);
multipliers[4] = new MulReceptiveField(4.0, 1.0);
multipliers[5] = new MulReceptiveField(5.0, 1.0);
multipliers[6] = new MulReceptiveField(6.0, 1.0);
multipliers[7] = new MulReceptiveField(7.0, 1.0);
multipliers[8] = new MulReceptiveField(8.0, 1.0);
multipliers[9] = new MulReceptiveField(9.0, 1.0);
// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
// 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
var arr = Enumerable.Range(0, 10)
.Select(i => (double)(i % 2) * 30.0)
.ToArray();
Assert.AreEqual(arr.Length, multipliers.Count);
var port = new NetworkSumOutputPort(arr, multipliers);
var sum = port.Get();
Assert.AreEqual(25.0, sum);
}
public void TestNetworkSumOutputPort()
{
var multipliers = new Dictionary<int, IReceptiveField>();
multipliers[0] = new MulReceptiveField(0.0, 1.0);
multipliers[1] = new MulReceptiveField(1.0, 1.0);
multipliers[2] = new MulReceptiveField(2.0, 1.0);
multipliers[3] = new MulReceptiveField(3.0, 1.0);
multipliers[4] = new MulReceptiveField(4.0, 1.0);
multipliers[5] = new MulReceptiveField(5.0, 1.0);
multipliers[6] = new MulReceptiveField(6.0, 1.0);
multipliers[7] = new MulReceptiveField(7.0, 1.0);
multipliers[8] = new MulReceptiveField(8.0, 1.0);
multipliers[9] = new MulReceptiveField(9.0, 1.0);
// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
// 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
var arr = Enumerable.Range(0, 10)
.Select(i => (double)(i % 2) * 30.0)
.ToArray();
Assert.AreEqual(arr.Length, multipliers.Count);
var port = new NetworkSumOutputPort(arr, multipliers);
var sum = port.Get();
Assert.AreEqual(25.0, sum);
}
public static NetworkPorts FromGenotype(NEAT.Genotype genotype)
{
var neuronGenes = genotype.NeuronGenes.ToList();
var synapseGenes = genotype.SynapseGenes.ToList();
var network = new Neural.Network(20ul);
var upperNeurons = new Dictionary <int, IReceptiveField>();
var lowerNeurons = new Dictionary <int, IReceptiveField>();
var positionNeurons = new Dictionary <int, IReceptiveField>();
var speedNeurons = new Dictionary <int, IReceptiveField>();
foreach (var neuronGene in neuronGenes)
{
float a = NumberHelper.Scale(neuronGene.a, 0.02f, 0.1f); // 0.1
float b = NumberHelper.Scale(neuronGene.b, 0.2f, 0.25f); // 0.2
float c = NumberHelper.Scale(neuronGene.c, -65.0f, -50.0f); // -65.0
float d = NumberHelper.Scale(neuronGene.d, 0.05f, 8.0f); // 2.0
try {
var id = (int)network.AddNeuron(Neural.IzhikevichConfig.Of(a, b, c, d));
var mean = 0.0f;
var sigma = 0.0f;
switch (neuronGene.type)
{
case NeuronType.UpperNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -180.0f, 180.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 180.0f);
upperNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.LowerNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -180.0f, 180.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 180.0f);
lowerNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.PositionNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -12.0f, 12.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 12.0f);
positionNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.SpeedNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -1.0f, 1.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 1000.0f);
speedNeurons[id] = new MulReceptiveField(mean, sigma);
break;
}
} catch (Exception e) {
Debug.LogException(e);
}
}
// Connect each input neuron to the output neuron.
foreach (var synapseGene in synapseGenes)
{
if (!synapseGene.isEnabled)
{
continue;
}
var fromNeuronId = neuronGenes.FindIndex(n => n.InnovationId == synapseGene.fromNeuronId);
var toNeuronId = neuronGenes.FindIndex(n => n.InnovationId == synapseGene.toNeuronId);
Assert.AreNotEqual(fromNeuronId, -1, "Must find from-neuron id");
Assert.AreNotEqual(toNeuronId, -1, "Must find to-neuron id");
float weight = NumberHelper.Scale(synapseGene.weight, -40.0f, 40.0f);
try {
network.AddSynapse((ulong)fromNeuronId, (ulong)toNeuronId, Neural.STDPConfig.Of(weight, -40.0f, 40.0f));
} catch (Exception e) {
Debug.LogException(e);
}
}
return(new NetworkPorts(network, upperNeurons, lowerNeurons, positionNeurons, speedNeurons));
}
public static NetworkPorts FromGenotype(NEAT.Genotype genotype)
{
var neuronGenes = genotype.NeuronGenes.ToList();
var synapseGenes = genotype.SynapseGenes.ToList();
var network = new Neural.Network(20ul);
var upperNeurons = new Dictionary<int, IReceptiveField>();
var lowerNeurons = new Dictionary<int, IReceptiveField>();
var positionNeurons = new Dictionary<int, IReceptiveField>();
var speedNeurons = new Dictionary<int, IReceptiveField>();
foreach (var neuronGene in neuronGenes) {
float a = NumberHelper.Scale(neuronGene.a, 0.02f, 0.1f); // 0.1
float b = NumberHelper.Scale(neuronGene.b, 0.2f, 0.25f); // 0.2
float c = NumberHelper.Scale(neuronGene.c, -65.0f, -50.0f); // -65.0
float d = NumberHelper.Scale(neuronGene.d, 0.05f, 8.0f); // 2.0
try {
var id = (int)network.AddNeuron(Neural.IzhikevichConfig.Of(a, b, c, d));
var mean = 0.0f;
var sigma = 0.0f;
switch (neuronGene.type) {
case NeuronType.UpperNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -180.0f, 180.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 180.0f);
upperNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.LowerNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -180.0f, 180.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 180.0f);
lowerNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.PositionNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -12.0f, 12.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 12.0f);
positionNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.SpeedNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -1.0f, 1.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 1000.0f);
speedNeurons[id] = new MulReceptiveField(mean, sigma);
break;
}
} catch (Exception e) {
Debug.LogException(e);
}
}
// Connect each input neuron to the output neuron.
foreach (var synapseGene in synapseGenes) {
if (!synapseGene.isEnabled) {
continue;
}
var fromNeuronId = neuronGenes.FindIndex(n => n.InnovationId == synapseGene.fromNeuronId);
var toNeuronId = neuronGenes.FindIndex(n => n.InnovationId == synapseGene.toNeuronId);
Assert.AreNotEqual(fromNeuronId, -1, "Must find from-neuron id");
Assert.AreNotEqual(toNeuronId, -1, "Must find to-neuron id");
float weight = NumberHelper.Scale(synapseGene.weight, -40.0f, 40.0f);
try {
network.AddSynapse((ulong)fromNeuronId, (ulong)toNeuronId, Neural.STDPConfig.Of(weight, -40.0f, 40.0f));
} catch (Exception e) {
Debug.LogException(e);
}
}
return new NetworkPorts(network, upperNeurons, lowerNeurons, positionNeurons, speedNeurons);
}
