static public INetwork DecodeToConcurrentNetwork(NeatGenome.NeatGenome g, IActivationFunction activationFn)
{
//----- Loop the neuronGenes. Create Neuron for each one.
// Store a table of neurons keyed by their id.
Hashtable neuronTable = new Hashtable(g.NeuronGeneList.Count);
NeuronList neuronList = new NeuronList();
foreach (NeuronGene neuronGene in g.NeuronGeneList)
{
Neuron newNeuron = new Neuron(activationFn, neuronGene.NeuronType, neuronGene.InnovationId);
neuronTable.Add(newNeuron.Id, newNeuron);
neuronList.Add(newNeuron);
}
//----- Loop the connection genes. Create a Connection for each one and bind them to the relevant Neurons.
foreach (ConnectionGene connectionGene in g.ConnectionGeneList)
{
Connection newConnection = new Connection(connectionGene.SourceNeuronId, connectionGene.TargetNeuronId, connectionGene.Weight);
// Bind the connection to it's source neuron.
newConnection.SetSourceNeuron((Neuron)neuronTable[connectionGene.SourceNeuronId]);
// Store the new connection against it's target neuron.
((Neuron)(neuronTable[connectionGene.TargetNeuronId])).ConnectionList.Add(newConnection);
}
return(new ConcurrentNetwork(neuronList));
}
static public INetwork DecodeToConcurrentNetwork(NeatGenome.NeatGenome g, IActivationFunction activationFn)
{
//----- Loop the neuronGenes. Create Neuron for each one.
// Store a table of neurons keyed by their id.
Hashtable neuronTable = new Hashtable(g.NeuronGeneList.Count);
NeuronList neuronList = new NeuronList();
foreach(NeuronGene neuronGene in g.NeuronGeneList)
{
Neuron newNeuron = new Neuron(activationFn, neuronGene.NeuronType, neuronGene.InnovationId);
neuronTable.Add(newNeuron.Id, newNeuron);
neuronList.Add(newNeuron);
}
//----- Loop the connection genes. Create a Connection for each one and bind them to the relevant Neurons.
foreach(ConnectionGene connectionGene in g.ConnectionGeneList)
{
Connection newConnection = new Connection(connectionGene.SourceNeuronId, connectionGene.TargetNeuronId, connectionGene.Weight);
// Bind the connection to it's source neuron.
newConnection.SetSourceNeuron((Neuron)neuronTable[connectionGene.SourceNeuronId]);
// Store the new connection against it's target neuron.
((Neuron)(neuronTable[connectionGene.TargetNeuronId])).ConnectionList.Add(newConnection);
}
return new ConcurrentNetwork(neuronList);
}
public void CreatedNeuronList()
{
NeuronList list = new NeuronList(42);
Assert.IsNotNull(list.Neurons);
Assert.AreEqual(42, list.Neurons.Count);
for (int k = 0; k < 42; k++)
{
Assert.IsNotNull(list.Neurons[k]);
}
}
public void CreateWeightedValue()
{
NeuronList list = new NeuronList(42);
var result = list.CreateWeightedValue();
Assert.IsNotNull(result);
Assert.IsNotNull(result.Inputs);
Assert.AreEqual(42, result.Inputs.Count);
for (int k = 0; k < 42; k++)
{
Assert.AreSame(list.Neurons[k], result.Inputs[k]);
Assert.IsTrue(result.Weights[k] >= -1.0);
Assert.IsTrue(result.Weights[k] <= 1.0);
Assert.IsTrue(result.Weights.Any(x => x != 0));
}
}
/// <summary>
/// Accepts a list of interconnected neurons that describe the network and loads them into this class instance
/// so that the network can be run. This primarily means placing input and output nodes into their own Lists
/// for use during the run.
/// </summary>
/// <param name="neuronList"></param>
private void LoadNeuronList(NeuronList neuronList)
{
masterNeuronList = neuronList;
int loopBound = masterNeuronList.Count;
for (int j = 0; j < loopBound; j++)
{
Neuron neuron = masterNeuronList[j];
switch (neuron.NeuronType)
{
case NeuronType.Input:
inputNeuronList.Add(neuron);
break;
case NeuronType.Output:
outputNeuronList.Add(neuron);
break;
}
}
}
public AbstractNetwork(NeuronList neuronList)
{
inputNeuronList = new NeuronList();
outputNeuronList = new NeuronList();
LoadNeuronList(neuronList);
}
public ConcurrentNetwork(NeuronList neuronList)
: base(neuronList)
{
}
private static ConcurrentNetwork ReadNetwork(XmlElement xmlNetwork)
{
//--- Read the activation function id.
string activationFnId = XmlUtilities.GetAttributeValue(xmlNetwork, "activation-fn-id");
IActivationFunction activationFn = ActivationFunctionFactory.GetActivationFunction(activationFnId);
// Read the neurons into a list and also into a table keyed on id.
Hashtable neuronTable = new Hashtable();
NeuronList biasNeuronList = new NeuronList();
NeuronList inputNeuronList = new NeuronList();
NeuronList hiddenNeuronList = new NeuronList();
NeuronList outputNeuronList = new NeuronList();
NeuronList masterNeuronList = new NeuronList();
XmlNodeList listNeurons = xmlNetwork.SelectNodes("neurons/neuron");
foreach (XmlElement xmlNeuron in listNeurons)
{
Neuron neuron = ReadNeuron(xmlNeuron);
neuronTable.Add(neuron.Id, neuron);
switch (neuron.NeuronType)
{
case NeuronType.Bias:
biasNeuronList.Add(neuron);
break;
case NeuronType.Input:
inputNeuronList.Add(neuron);
break;
case NeuronType.Hidden:
hiddenNeuronList.Add(neuron);
break;
case NeuronType.Output:
outputNeuronList.Add(neuron);
break;
}
}
//----- Build a master list of neurons. Neurons must be ordered by type - bias,input,hidden,output.
if (biasNeuronList.Count != 1)
{
throw new SharpNeatLib.Xml.XmlException("Neural Network XML must contain exactly 1 bias node.");
}
foreach (Neuron neuron in biasNeuronList)
{
masterNeuronList.Add(neuron);
}
foreach (Neuron neuron in inputNeuronList)
{
masterNeuronList.Add(neuron);
}
foreach (Neuron neuron in hiddenNeuronList)
{
masterNeuronList.Add(neuron);
}
foreach (Neuron neuron in outputNeuronList)
{
masterNeuronList.Add(neuron);
}
//----- Read Connections and store against target neurons.
XmlNodeList listConnections = xmlNetwork.SelectNodes("connections/connection");
foreach (XmlElement xmlConnection in listConnections)
{
Connection connection = ReadConnection(xmlConnection);
// Store the connection with it's target neuron.
((Neuron)neuronTable[connection.TargetNeuronId]).ConnectionList.Add(connection);
// Bind the connection to it's source neuron.
connection.SetSourceNeuron((Neuron)neuronTable[connection.SourceNeuronId]);
}
return(new ConcurrentNetwork(masterNeuronList));
}
public AbstractNetwork(NeuronList neuronList)
{
inputNeuronList = new NeuronList();
outputNeuronList = new NeuronList();
LoadNeuronList(neuronList);
}
/// <summary>
/// Accepts a list of interconnected neurons that describe the network and loads them into this class instance
/// so that the network can be run. This primarily means placing input and output nodes into their own Lists
/// for use during the run.
/// </summary>
/// <param name="neuronList"></param>
private void LoadNeuronList(NeuronList neuronList)
{
masterNeuronList = neuronList;
int loopBound = masterNeuronList.Count;
for(int j=0; j<loopBound; j++)
{
Neuron neuron = masterNeuronList[j];
switch(neuron.NeuronType)
{
case NeuronType.Input:
inputNeuronList.Add(neuron);
break;
case NeuronType.Output:
outputNeuronList.Add(neuron);
break;
}
}
}
private static ConcurrentNetwork ReadNetwork(XmlElement xmlNetwork)
{
//--- Read the activation function id.
string activationFnId = XmlUtilities.GetAttributeValue(xmlNetwork, "activation-fn-id");
IActivationFunction activationFn = ActivationFunctionFactory.GetActivationFunction(activationFnId);
// Read the neurons into a list and also into a table keyed on id.
Hashtable neuronTable = new Hashtable();
NeuronList biasNeuronList = new NeuronList();
NeuronList inputNeuronList = new NeuronList();
NeuronList hiddenNeuronList = new NeuronList();
NeuronList outputNeuronList = new NeuronList();
NeuronList masterNeuronList = new NeuronList();
XmlNodeList listNeurons = xmlNetwork.SelectNodes("neurons/neuron");
foreach(XmlElement xmlNeuron in listNeurons)
{
Neuron neuron = ReadNeuron(xmlNeuron);
neuronTable.Add(neuron.Id, neuron);
switch(neuron.NeuronType)
{
case NeuronType.Bias:
biasNeuronList.Add(neuron);
break;
case NeuronType.Input:
inputNeuronList.Add(neuron);
break;
case NeuronType.Hidden:
hiddenNeuronList.Add(neuron);
break;
case NeuronType.Output:
outputNeuronList.Add(neuron);
break;
}
}
//----- Build a master list of neurons. Neurons must be ordered by type - bias,input,hidden,output.
if(biasNeuronList.Count != 1)
throw new SharpNeatLib.Xml.XmlException("Neural Network XML must contain exactly 1 bias node.");
foreach(Neuron neuron in biasNeuronList)
masterNeuronList.Add(neuron);
foreach(Neuron neuron in inputNeuronList)
masterNeuronList.Add(neuron);
foreach(Neuron neuron in hiddenNeuronList)
masterNeuronList.Add(neuron);
foreach(Neuron neuron in outputNeuronList)
masterNeuronList.Add(neuron);
//----- Read Connections and store against target neurons.
XmlNodeList listConnections = xmlNetwork.SelectNodes("connections/connection");
foreach(XmlElement xmlConnection in listConnections)
{
Connection connection = ReadConnection(xmlConnection);
// Store the connection with it's target neuron.
((Neuron)neuronTable[connection.TargetNeuronId]).ConnectionList.Add(connection);
// Bind the connection to it's source neuron.
connection.SetSourceNeuron((Neuron)neuronTable[connection.SourceNeuronId]);
}
return new ConcurrentNetwork(masterNeuronList);
}
public ConcurrentNetwork(NeuronList neuronList)
: base(neuronList)
{
}
