/// <summary>
/// See if the best genome has changed, and decode a new best network, if
/// needed.
/// </summary>
private void UpdateBestNetwork()
{
if (BestGenome != _cachedBestGenome)
{
_cachedBestGenome = BestGenome;
_bestNetwork = (NEATNetwork)CODEC.Decode(BestGenome);
}
}
/// <inheritdoc/>
public IMLMethod Decode(IGenome genome)
{
var neatGenome = (NEATGenome)genome;
var pop = (NEATPopulation)neatGenome.Population;
IList <NEATNeuronGene> neuronsChromosome = neatGenome.NeuronsChromosome;
IList <NEATLinkGene> linksChromosome = neatGenome.LinksChromosome;
if (neuronsChromosome[0].NeuronType != NEATNeuronType.Bias)
{
throw new NeuralNetworkError(
"The first neuron must be the bias neuron, this genome is invalid.");
}
var links = new List <NEATLink>();
var afs = new IActivationFunction[neuronsChromosome.Count];
for (int i = 0; i < afs.Length; i++)
{
afs[i] = neuronsChromosome[i].ActivationFunction;
}
IDictionary <long, int> lookup = new Dictionary <long, int>();
for (int i = 0; i < neuronsChromosome.Count; i++)
{
NEATNeuronGene neuronGene = neuronsChromosome[i];
lookup[neuronGene.Id] = i;
}
// loop over connections
for (int i = 0; i < linksChromosome.Count; i++)
{
NEATLinkGene linkGene = linksChromosome[i];
if (linkGene.Enabled)
{
links.Add(new NEATLink(lookup[linkGene.FromNeuronId],
lookup[linkGene.ToNeuronId], linkGene.Weight));
}
}
links.Sort();
NEATNetwork network = new NEATNetwork(neatGenome.InputCount,
neatGenome.OutputCount, links, afs);
network.ActivationCycles = pop.ActivationCycles;
return(network);
}
public virtual object Read(Stream mask0)
{
EncogReadHelper helper;
EncogFileSection section;
IDictionary<int, NEATNeuron> dictionary;
IDictionary<string, string> dictionary2;
double num2;
double num3;
int num5;
int num6;
bool flag;
double num7;
NEATNetwork network = new NEATNetwork();
if ((((uint) flag) + ((uint) num3)) <= uint.MaxValue)
{
if ((((uint) num6) + ((uint) num6)) < 0)
{
goto Label_03FB;
}
if ((((uint) flag) + ((uint) num3)) <= uint.MaxValue)
{
helper = new EncogReadHelper(mask0);
if ((((uint) flag) & 0) == 0)
{
dictionary = new Dictionary<int, NEATNeuron>();
goto Label_0023;
}
}
goto Label_03AA;
}
goto Label_0035;
Label_0023:
if ((section = helper.ReadNextSection()) != null)
{
goto Label_045C;
}
return network;
Label_0035:
if (section.SectionName.Equals("NEAT"))
{
while (section.SubSectionName.Equals("LINKS"))
{
using (IEnumerator<string> enumerator3 = section.Lines.GetEnumerator())
{
string str3;
IList<string> list2;
NEATNeuron neuron3;
NEATLink link;
goto Label_007D;
Label_005B:
if ((((uint) num6) & 0) != 0)
{
goto Label_00D5;
}
neuron3.InboundLinks.Add(link);
Label_007D:
if (enumerator3.MoveNext())
{
goto Label_00FA;
}
if ((((uint) num2) + ((uint) num3)) >= 0)
{
goto Label_0023;
}
Label_009E:
num7 = CSVFormat.EgFormat.Parse(list2[3]);
NEATNeuron fromNeuron = dictionary[num5];
neuron3 = dictionary[num6];
link = new NEATLink(num7, fromNeuron, neuron3, flag);
Label_00D5:
fromNeuron.OutputboundLinks.Add(link);
if (((uint) num3) >= 0)
{
goto Label_005B;
}
goto Label_0023;
Label_00FA:
str3 = enumerator3.Current;
list2 = EncogFileSection.SplitColumns(str3);
num5 = int.Parse(list2[0]);
if (-2 != 0)
{
}
num6 = int.Parse(list2[1]);
flag = int.Parse(list2[2]) > 0;
goto Label_009E;
}
}
if (((uint) num7) >= 0)
{
}
}
goto Label_0023;
Label_03AA:
if (!section.SectionName.Equals("NEAT") || !section.SubSectionName.Equals("NETWORK"))
{
if (section.SectionName.Equals("NEAT") && section.SubSectionName.Equals("NEURONS"))
{
using (IEnumerator<string> enumerator2 = section.Lines.GetEnumerator())
{
string str2;
IList<string> list;
long num;
NEATNeuronType type;
NEATNeuron neuron;
Label_01BD:
if (enumerator2.MoveNext())
{
goto Label_0257;
}
goto Label_0023;
Label_01CE:
if ((((uint) num) + ((uint) num5)) > uint.MaxValue)
{
goto Label_02A0;
}
network.Neurons.Add(neuron);
dictionary[(int) num] = neuron;
goto Label_02D0;
Label_0206:
num3 = CSVFormat.EgFormat.Parse(list[3]);
double num4 = CSVFormat.EgFormat.Parse(list[4]);
neuron = new NEATNeuron(type, num, num3, num4, num2);
if ((((uint) num7) - ((uint) num)) <= uint.MaxValue)
{
goto Label_02B9;
}
Label_0257:
str2 = enumerator2.Current;
do
{
list = EncogFileSection.SplitColumns(str2);
num = int.Parse(list[0]);
type = PersistNEATPopulation.StringToNeuronType(list[1]);
}
while ((((uint) num) + ((uint) num5)) < 0);
Label_02A0:
num2 = CSVFormat.EgFormat.Parse(list[2]);
goto Label_0206;
Label_02B9:
if ((((uint) num3) & 0) == 0)
{
goto Label_01CE;
}
Label_02D0:
if ((((uint) num6) - ((uint) num4)) >= 0)
{
goto Label_01BD;
}
goto Label_0023;
}
}
goto Label_0035;
}
IDictionary<string, string> paras = section.ParseParams();
network.InputCount = EncogFileSection.ParseInt(paras, "inputCount");
network.OutputCount = EncogFileSection.ParseInt(paras, "outputCount");
network.ActivationFunction = EncogFileSection.ParseActivationFunction(paras, "activationFunction");
if (((uint) num7) <= uint.MaxValue)
{
network.OutputActivationFunction = EncogFileSection.ParseActivationFunction(paras, "outAct");
if (((uint) num3) >= 0)
{
network.NetworkDepth = EncogFileSection.ParseInt(paras, "depth");
network.Snapshot = EncogFileSection.ParseBoolean(paras, "snapshot");
goto Label_0023;
}
goto Label_0035;
}
Label_03F3:
dictionary2 = section.ParseParams();
Label_03FB:
foreach (string str in dictionary2.Keys)
{
network.Properties.Add(str, dictionary2[str]);
}
goto Label_03AA;
if (((((uint) flag) + ((uint) num2)) <= uint.MaxValue) && (((uint) flag) >= 0))
{
goto Label_03AA;
}
Label_045C:
if (section.SectionName.Equals("NEAT") && section.SubSectionName.Equals("PARAMS"))
{
goto Label_03F3;
}
goto Label_03AA;
}
/// <summary>
/// See if the best genome has changed, and decode a new best network, if
/// needed.
/// </summary>
private void UpdateBestNetwork()
{
if (BestGenome != _cachedBestGenome)
{
_cachedBestGenome = BestGenome;
_bestNetwork = (NEATNetwork)CODEC.Decode(BestGenome);
}
}
/// <summary>
/// Read the object.
/// </summary>
/// <param name="mask0">The stream to read from.</param>
/// <returns>The loaded object.</returns>
public virtual Object Read(Stream mask0)
{
var result = new NEATNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
IDictionary<Int32, NEATNeuron> neuronMap = new Dictionary<Int32, NEATNeuron>();
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT")
&& section.SubSectionName.Equals("PARAMS"))
{
IDictionary<String, String> paras = section.ParseParams();
foreach (String key in paras.Keys)
{
result.Properties.Add(key, paras[key]);
}
}
if (section.SectionName.Equals("NEAT")
&& section.SubSectionName.Equals("NETWORK"))
{
IDictionary<String, String> p = section.ParseParams();
result.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(p,
PersistConst.OutputCount);
result.ActivationFunction = EncogFileSection
.ParseActivationFunction(p,
PersistConst.ActivationFunction);
result.OutputActivationFunction = EncogFileSection
.ParseActivationFunction(p,
NEATPopulation.PropertyOutputActivation);
result.NetworkDepth = EncogFileSection.ParseInt(p,
PersistConst.Depth);
result.Snapshot = EncogFileSection.ParseBoolean(p,
PersistConst.Snapshot);
}
else if (section.SectionName.Equals("NEAT")
&& section.SubSectionName.Equals("NEURONS"))
{
foreach (String line in section.Lines)
{
IList<String> cols = EncogFileSection.SplitColumns(line);
long neuronID = Int32.Parse(cols[0]);
NEATNeuronType neuronType = PersistNEATPopulation
.StringToNeuronType(cols[1]);
double activationResponse = CSVFormat.EgFormat
.Parse(cols[2]);
double splitY = CSVFormat.EgFormat
.Parse(cols[3]);
double splitX = CSVFormat.EgFormat
.Parse(cols[4]);
var neatNeuron = new NEATNeuron(neuronType,
neuronID, splitY, splitX, activationResponse);
result.Neurons.Add(neatNeuron);
neuronMap[((int) neuronID)] = (neatNeuron);
}
}
else if (section.SectionName.Equals("NEAT")
&& section.SubSectionName.Equals("LINKS"))
{
foreach (String line in section.Lines)
{
IList<String> cols = EncogFileSection.SplitColumns(line);
int fromID = Int32.Parse(cols[0]);
int toID = Int32.Parse(cols[1]);
bool recurrent = Int32.Parse(cols[2]) > 0;
double weight = CSVFormat.EgFormat.Parse(cols[3]);
NEATNeuron fromNeuron = (neuronMap[fromID]);
NEATNeuron toNeuron = (neuronMap[toID]);
var neatLink = new NEATLink(weight, fromNeuron,
toNeuron, recurrent);
fromNeuron.OutputboundLinks.Add(neatLink);
toNeuron.InboundLinks.Add(neatLink);
}
}
}
return result;
}
private NEATNetwork Create()
{
IList<NEATNeuron> neurons = new List<NEATNeuron>();
IActivationFunction afSigmoid = new ActivationSigmoid();
IActivationFunction afStep = new ActivationStep();
// create the neurons
NEATNeuron input1 = new NEATNeuron(
NEATNeuronType.Input,
1,
0.1,
0.2,
0.3);
NEATNeuron input2 = new NEATNeuron(
NEATNeuronType.Input,
2,
0.1,
0.2,
0.3);
NEATNeuron bias = new NEATNeuron(
NEATNeuronType.Bias,
3,
0.1,
0.2,
0.3);
NEATNeuron hidden1 = new NEATNeuron(
NEATNeuronType.Hidden,
4,
0.1,
0.2,
0.3);
NEATNeuron output = new NEATNeuron(
NEATNeuronType.Output,
5,
0.1,
0.2,
0.3);
// add the neurons
neurons.Add(input1);
neurons.Add(input2);
neurons.Add(hidden1);
neurons.Add(bias);
neurons.Add(output);
// connect everything
Link(0.01, input1, hidden1, false);
Link(0.01, input2, hidden1, false);
Link(0.01, bias, hidden1, false);
Link(0.01, hidden1, output, false);
// create the network
NEATNetwork result = new NEATNetwork(2,
1,
neurons,
afSigmoid,
afStep,
3);
return result;
}
public void SortAndRecord()
{
foreach (IGenome genome in base.Population.Genomes)
{
genome.Decode();
base.PerformCalculateScore(genome);
}
while (true)
{
base.Population.Sort();
do
{
IGenome best = base.Population.Best;
double score = best.Score;
if (base.Comparator.IsBetterThan(score, this.x411ff254761a2325))
{
this.x411ff254761a2325 = score;
this.xc98330799092ad50 = (NEATNetwork) best.Organism;
}
this.x411ff254761a2325 = base.Comparator.BestScore(this.Error, this.x411ff254761a2325);
}
while (0x7fffffff == 0);
if (0 == 0)
{
return;
}
}
}
private void Validate(NEATNetwork network)
{
Assert.AreEqual(1, network.OutputCount);
Assert.AreEqual(2, network.InputCount);
Assert.AreEqual(3, network.NetworkDepth);
Assert.IsTrue(network.ActivationFunction is ActivationSigmoid);
Assert.IsTrue(network.OutputActivationFunction is ActivationStep);
Assert.AreEqual(0.0, ((ActivationStep)network.OutputActivationFunction).Center);
Assert.AreEqual(1.0, ((ActivationStep)network.OutputActivationFunction).High);
Assert.AreEqual(0.0, ((ActivationStep)network.OutputActivationFunction).Low);
Assert.AreEqual(5, network.Neurons.Count);
IDictionary<NEATNeuronType, NEATNeuron> neurons = new Dictionary<NEATNeuronType, NEATNeuron>();
foreach (NEATNeuron neuron in network.Neurons)
{
neurons[neuron.NeuronType] = neuron;
}
Assert.AreEqual(4, neurons.Count);
NEATNeuron output = neurons[NEATNeuronType.Output];
NEATNeuron input = neurons[NEATNeuronType.Input];
Assert.AreEqual(1, input.OutputboundLinks.Count);
Assert.AreEqual(0, input.InboundLinks.Count);
Assert.AreEqual(0, output.OutputboundLinks.Count);
Assert.AreEqual(1, output.InboundLinks.Count);
}
/// <inheritdoc/>
public IMLMethod Decode(IGenome genome)
{
var neatGenome = (NEATGenome)genome;
var pop = (NEATPopulation)neatGenome.Population;
IList<NEATNeuronGene> neuronsChromosome = neatGenome.NeuronsChromosome;
IList<NEATLinkGene> linksChromosome = neatGenome.LinksChromosome;
if (neuronsChromosome[0].NeuronType != NEATNeuronType.Bias)
{
throw new NeuralNetworkError(
"The first neuron must be the bias neuron, this genome is invalid.");
}
var links = new List<NEATLink>();
var afs = new IActivationFunction[neuronsChromosome.Count];
for (int i = 0; i < afs.Length; i++)
{
afs[i] = neuronsChromosome[i].ActivationFunction;
}
IDictionary<long, int> lookup = new Dictionary<long, int>();
for (int i = 0; i < neuronsChromosome.Count; i++)
{
NEATNeuronGene neuronGene = neuronsChromosome[i];
lookup[neuronGene.Id] = i;
}
// loop over connections
for (int i = 0; i < linksChromosome.Count; i++)
{
NEATLinkGene linkGene = linksChromosome[i];
if (linkGene.Enabled)
{
links.Add(new NEATLink(lookup[linkGene.FromNeuronId],
lookup[linkGene.ToNeuronId], linkGene.Weight));
}
}
links.Sort();
NEATNetwork network = new NEATNetwork(neatGenome.InputCount,
neatGenome.OutputCount, links, afs);
network.ActivationCycles = pop.ActivationCycles;
return network;
}
/// <summary>
/// Read the object.
/// </summary>
/// <param name="mask0">The stream to read from.</param>
/// <returns>The loaded object.</returns>
public virtual Object Read(Stream mask0)
{
var result = new NEATNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
IDictionary <Int32, NEATNeuron> neuronMap = new Dictionary <Int32, NEATNeuron>();
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
foreach (String key in paras.Keys)
{
result.Properties.Add(key, paras[key]);
}
}
if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(p,
PersistConst.OutputCount);
result.ActivationFunction = EncogFileSection
.ParseActivationFunction(p,
PersistConst.ActivationFunction);
result.OutputActivationFunction = EncogFileSection
.ParseActivationFunction(p,
NEATPopulation.PropertyOutputActivation);
result.NetworkDepth = EncogFileSection.ParseInt(p,
PersistConst.Depth);
result.Snapshot = EncogFileSection.ParseBoolean(p,
PersistConst.Snapshot);
}
else if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("NEURONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
long neuronID = Int32.Parse(cols[0]);
NEATNeuronType neuronType = PersistNEATPopulation
.StringToNeuronType(cols[1]);
double activationResponse = CSVFormat.EgFormat
.Parse(cols[2]);
double splitY = CSVFormat.EgFormat
.Parse(cols[3]);
double splitX = CSVFormat.EgFormat
.Parse(cols[4]);
var neatNeuron = new NEATNeuron(neuronType,
neuronID, splitY, splitX, activationResponse);
result.Neurons.Add(neatNeuron);
neuronMap[((int)neuronID)] = (neatNeuron);
}
}
else if (section.SectionName.Equals("NEAT") &&
section.SubSectionName.Equals("LINKS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
int fromID = Int32.Parse(cols[0]);
int toID = Int32.Parse(cols[1]);
bool recurrent = Int32.Parse(cols[2]) > 0;
double weight = CSVFormat.EgFormat.Parse(cols[3]);
NEATNeuron fromNeuron = (neuronMap[fromID]);
NEATNeuron toNeuron = (neuronMap[toID]);
var neatLink = new NEATLink(weight, fromNeuron,
toNeuron, recurrent);
fromNeuron.OutputboundLinks.Add(neatLink);
toNeuron.InboundLinks.Add(neatLink);
}
}
}
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;
}
public override void Decode()
{
int elementPos;
NEATNetwork network;
NEATPopulation population = (NEATPopulation) base.Population;
IList<NEATNeuron> neurons = new List<NEATNeuron>();
if ((((uint) elementPos) & 0) == 0)
{
foreach (IGene gene in this.Neurons.Genes)
{
NEATNeuronGene gene2 = (NEATNeuronGene) gene;
NEATNeuron item = new NEATNeuron(gene2.NeuronType, gene2.Id, gene2.SplitY, gene2.SplitX, gene2.ActivationResponse);
do
{
neurons.Add(item);
}
while (((uint) elementPos) < 0);
}
}
using (List<IGene>.Enumerator enumerator2 = this.Links.Genes.GetEnumerator())
{
IGene gene3;
NEATLinkGene gene4;
NEATNeuron neuron2;
NEATNeuron neuron3;
NEATLink link;
goto Label_00D5;
Label_00A0:
link = new NEATLink(gene4.Weight, neuron2, neuron3, gene4.Recurrent);
neuron2.OutputboundLinks.Add(link);
neuron3.InboundLinks.Add(link);
Label_00D5:
if (enumerator2.MoveNext())
{
goto Label_0159;
}
goto Label_01CA;
Label_00E3:
Console.Out.WriteLine("test");
goto Label_0111;
Label_00F4:
if (elementPos == -1)
{
goto Label_00E3;
}
if ((((uint) elementPos) | 15) == 0)
{
goto Label_0147;
}
Label_0111:
neuron3 = neurons[elementPos];
if ((((uint) elementPos) - ((uint) elementPos)) > uint.MaxValue)
{
goto Label_00F4;
}
goto Label_00A0;
Label_013D:
neuron2 = neurons[elementPos];
Label_0147:
elementPos = this.GetElementPos((long) gene4.ToNeuronID);
goto Label_00F4;
Label_0159:
gene3 = enumerator2.Current;
gene4 = (NEATLinkGene) gene3;
if (!gene4.Enabled)
{
goto Label_00D5;
}
elementPos = this.GetElementPos((long) gene4.FromNeuronID);
if ((((uint) elementPos) - ((uint) elementPos)) <= uint.MaxValue)
{
goto Label_013D;
}
if ((((uint) elementPos) - ((uint) elementPos)) >= 0)
{
goto Label_00E3;
}
}
Label_01CA:
network = new NEATNetwork(this.inputCount, this.outputCount, neurons, population.NeatActivationFunction, population.OutputActivationFunction, 0);
network.Snapshot = population.Snapshot;
base.Organism = network;
}
