private void adHocAnalysisToolStripMenuItem_Click(object sender, EventArgs e)
{
string filename;
StreamWriter sw;
NeatGenome.NeatGenome seedGenome = null;
Stats currentStats = new Stats();
for (FoodGatherParams.resolution = 8; FoodGatherParams.resolution <= 128; FoodGatherParams.resolution *= 2)
{
FoodGatherParams.fillFood();
FoodGatherParams.fillLookups();
sw = new StreamWriter("logfile" + FoodGatherParams.resolution.ToString() + ".txt");
for (int run = 1; run <= 20; run++)
{
for (int generation = 1; generation <= 500; generation++)
{
filename = "run" + run.ToString() + @"\genome_" + generation.ToString() + ".xml";
try
{
XmlDocument doc = new XmlDocument();
doc.Load("run" + run.ToString() + "\\genome_" + generation.ToString() + ".xml");
seedGenome = XmlNeatGenomeReaderStatic.Read(doc);
}
catch (Exception ex)
{
//MessageBox.Show(generation.ToString());
currentStats.generation = generation;
currentStats.run = run;
writeStats(sw, currentStats);
continue;
//do some output
}
currentStats = FoodGathererNetworkEvaluator.postHocAnalyzer(seedGenome);
currentStats.CPPNconnections = seedGenome.ConnectionGeneList.Count;
currentStats.CPPNneurons = seedGenome.NeuronGeneList.Count;
currentStats.generation = generation;
currentStats.run = run;
writeStats(sw, currentStats);
//break;
}
// sw.Flush();
}
sw.Close();
}
}
public override void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
FoodGatherParams.fillLookups();
FoodGatherParams.fillFood();
int count = pop.GenomeList.Count;
for (int i = 0; i < count; i++)
{
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
INetwork network = g.Decode(activationFn);
if (network == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
g.Fitness = Math.Max(networkEvaluator.EvaluateNetwork(network), EvolutionAlgorithm.MIN_GENOME_FITNESS);
g.ObjectiveFitness = g.Fitness;
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
}
if (requestResolutionUp == true)
{
requestResolutionUp = false;
requestResolutionDown = false;
FoodGatherParams.resolution *= 2;
}
else if (requestResolutionDown == true)
{
requestResolutionUp = false;
requestResolutionDown = false;
if (FoodGatherParams.resolution > 4)
{
FoodGatherParams.resolution /= 2;
}
}
}
private void playWithNetworkToolStripMenuItem_Click(object sender, EventArgs e)
{
FoodGatherParams.fillLookups();
if (currentBest != null)
{
board = new Board(0, 500);
Robot r;
if (FoodGatherParams.circle)
{
r = new Robot(new PointF(250, 250), (int)FoodGatherParams.resolution, FoodGathererNetworkEvaluator.substrate.generateNetwork(currentBest));
}
else
{
r = new Robot(new PointF(250, 250), (int)FoodGatherParams.resolution, FoodGathererNetworkEvaluator.substrate.generateNetwork(currentBest));
}
board.AddRobot(r);
this.Refresh();
}
}
private void Demo()
{
FoodGatherParams.fillLookups();
FoodGatherParams.fillFood();
for (int j = 0; j < FoodGatherParams.foodLocations.Length; j++)
{
Robot r;
board = new Board(0, 500);
if (FoodGatherParams.circle)
{
r = new Robot(new PointF(250, 250), (int)FoodGatherParams.resolution, FoodGathererNetworkEvaluator.substrate.generateNetwork(currentBest));
}
else
{
r = new Robot(new PointF(250, 250), (int)FoodGatherParams.resolution, FoodGathererNetworkEvaluator.substrate.generateNetwork(currentBest));
}
board.AddRobot(r);
board.AddFood(new Food(FoodGatherParams.foodLocations[j]));
board.gameView(this);
}
}