/// <summary>
/// Save network to file
/// </summary>
/// <param name="network">Network to save</param>
/// <param name="filename">FileName</param>
public static void SaveNetwork(SNP_Network network, string filename)
{
Console.WriteLine("----------- Saving Network -----------");
string json = ConvertNetworkToJson(network);
SaveToFile(json, filename);
}
/// <summary>
/// compare the fitnesses of the two networks, save the best genes
/// </summary>
private void CalculateFitness()
{
fitnessSum = 0;
DNA best = Population[0];
for (int i = 0; i < Population.Count; i++)
{
fitnessSum += Population[i].CalculateFitness(i);
if (Generation > 1 && Population[i].Fitness >= 0 && !float.IsNaN(Population[i].Fitness) && !Population[i].Equals(null) && Generation > 1 && Population[i].Fitness <= 1)
{
Console.WriteLine("Current Fitness: {0},", Population[i].Fitness);
FitnessList.Add(Population[i].Fitness);
}
if (Population[i].Fitness >= best.Fitness && Generation > 1)
{
best = Population[i];
best.Genes.minifiedPrint();
best.Genes.OutputSet.ForEach(x => Console.Write("{0}\t", x));
Console.WriteLine("\nCurrent best fitness: {0}", best.Fitness);
// FitnessList.Add(best.Fitness);
}
}
BestFitness = best.Fitness;
BestGenes = ReflectionCloner.DeepFieldClone(best.Genes);
}
/// <summary>
/// This code will loop over the entire network and remove any spikes which match the correct rules.
/// </summary>
/// <param name="networkRef">the network which this neuron belongs to and for which this method is being executed</param>
/// <param name="Connections">connections that this neuron has</param>
/// <returns></returns>
public bool?RemoveSpikes(SNP_Network networkRef)
{
int index;
if (this.ActiveDelay == 0)
{
if (PersistedState.Equals(true))
{
this.SpikeCount = "";
PersistedState = false;
return(true);
}
else if (PersistedState.Equals(null))
{
this.SpikeCount = "";
PersistedState = false;
return(null);
}
else
{
index = MatchRules();
if (this.Rules[index].IsMatched(this.SpikeCount).Equals(null))
{
if (this.Rules[index].Delay > 0)
{
this.ActiveDelay = this.Rules[index].Delay;
this.PersistedState = null;
return(false);
}
this.SpikeCount = "";
return(null);
}
else if (this.Rules[index].IsMatched(this.SpikeCount).Equals(false))
{
return(false);
}
else if (this.Rules[index].IsMatched(this.SpikeCount).Equals(true))
{
if (this.Rules[index].Delay > 0)
{
this.ActiveDelay = this.Rules[index].Delay;
this.PersistedState = true;
return(false);
}
this.SpikeCount = "";
return(true);
}
}
}
else
{
this.ActiveDelay--;
return(false);
}
// this should never happen.
Console.Error.WriteLine("Foreach loop failed.");
return(false);
}
/// <summary>
/// Once the spikes have been removed, each network can spike across all of its connections
/// </summary>
/// <param name="networkRef">the network which this neuron belongs to and for which this method is being executed</param>
/// <param name="Connections">connections that this neuron has</param>
public void FireSpike(SNP_Network networkRef, List <int> Connections)
{
/*
* If List has more than one rule
* Check if the neuron has enough spike to satisfy some or all of the rules
* If only one rule can proceed, complete that spike
* If more than one rule can proceed at any one time use the Random() function to determine which will fire
* Fire the spike on that chosen rule definition
* We do not need to worry about the removal of spikes as that is done in RemoveSpikes()
*/
int index = MatchRules();
foreach (int connection in Connections)
{
if (this.Rules[index].IsMatched(this.SpikeCount).Equals(true))
{
if (this.Rules[index].Fire)
{
networkRef.Neurons[connection - 1].SpikeCount = networkRef.Neurons[connection - 1].SpikeCount + "a";
}
}
}
}
/// <summary>
/// Convert the provided SN P Network to a JSON format for exporting
/// </summary>
/// <param name="network">SNP network to convert</param>
/// <returns>JSON String</returns>
public static string ConvertNetworkToJson(SNP_Network network)
{
return(JsonConvert.SerializeObject(network, Formatting.Indented));
}
