public void PrintStats()
{
// MUST BE CALLED AFTER RunEA
NeatAlgorithmStats stats = contentEA.Statistics;
Debug.Log("************* NEAT STATS **************");
Debug.Log("Generation = " + stats._generation + ", Total Evaluation = " + stats._totalEvaluationCount
+ ", Max Fitness = " + stats._maxFitness + ", MeanFitness = " + stats._meanFitness);
IGenomeDecoder <NeatGenome, IBlackBox> decoder = experiment.CreateGenomeDecoder();
IBlackBox box = decoder.Decode(contentEA.CurrentChampGenome);
FastAcyclicNetwork concrete = (FastAcyclicNetwork)box;
Debug.Log("Champ:Num hidden nodes = " + concrete.hiddenNodeCount + ", num connections = " + concrete.connectionCount);
// Get highlevel features
PCGSharpHighLevelFeatures featureCounts = new PCGSharpHighLevelFeatures();
PCGSharpHighLevelFeatures.C_HL_ROOMTYPE lastRoomType = PCGSharpHighLevelFeatures.C_HL_ROOMTYPE.NoPreviousRoom;
for (int i = 0; i < (experiment as PCGNeatExperiment).geomNodeList.Count; i++)
{
// Get cppn output for each node
box.InputSignalArray[0] = (experiment as PCGNeatExperiment).geomNodeList[i].normDepth;
box.InputSignalArray[1] = (experiment as PCGNeatExperiment).geomNodeList[i].normSiblingIndex;
box.ResetState();
box.Activate();
string outputString = box.OutputSignalArray[0].ToString();
double[] outputs = new double[box.OutputCount];
outputs[0] = box.OutputSignalArray[0];
for (int j = 1; j < box.OutputCount; j++)
{
outputString = outputString + "," + box.OutputSignalArray[j];
outputs[j] = box.OutputSignalArray[j];
}
Debug.Log("(" + (experiment as PCGNeatExperiment).geomNodeList[i].normDepth + ","
+ (experiment as PCGNeatExperiment).geomNodeList[i].normSiblingIndex + ") -> (" + outputString + ")");
// Convert each nodes cppn output into a contentId
int combinedContentID = featureCounts.CPPNOutputToSettingsId(outputs);
lastRoomType = featureCounts.UpdateFeatures(combinedContentID, lastRoomType);
}
// Pass the highlevel features into the player model to get the fitness
if ((experiment as PCGNeatExperiment).playerModel != null)
{
double[] distributions = (experiment as PCGNeatExperiment).playerModel.ClassifyNewData(featureCounts.ToDoubleArray());
Debug.Log("Classifier: Champ Distributions: Dislike = " + distributions[0] + ", Like = " + distributions[1]);
(experiment as PCGNeatExperiment).playerModel.PrintClassifierTestReport();
}
Debug.Log("**************END NEAT STATS**************");
}
// Use this for initialization
IEnumerator Start()
{
/*** Initialize experiment ***/
INeatExperiment experiment = new TestExperiment() as INeatExperiment;
// Null because not reading settings from xmlFile
experiment.Initialize("this is a test", null);
/*** Randomly generate population ***/
// Set initial settings
// ? means it is nullable
int?popSize = experiment.DefaultPopulationSize;
//double? initConnProportion = experiment.NeatGenomeParameters.InitialInterconnectionsProportion;
// Create a genome factory appropriate for the experiment.
IGenomeFactory <NeatGenome> genomeFactory = experiment.CreateGenomeFactory();
// Create an initial population of randomly generated genomes.
// 0u is a struct for a 32 bit unsigned integer
List <NeatGenome> genomeList = genomeFactory.CreateGenomeList(popSize.Value, 0u);
// Check number of species is <= the number of the genomes.
if (genomeList.Count < experiment.NeatEvolutionAlgorithmParameters.SpecieCount)
{
Debug.Log("Genome count must be >= specie count. Genomes=" + genomeList.Count
+ " Species=" + experiment.NeatEvolutionAlgorithmParameters.SpecieCount);
return(false);
}
/*** Run the algorithm ***/
ea = experiment.CreateEvolutionAlgorithm(genomeFactory, genomeList);
//for (int j = 0; j < 100; j++) {
yield return(new WaitForSeconds(0.5f));
//Debug.Log(j);
ea.StartContinue();
NeatAlgorithmStats stats = ea.Statistics;
Debug.Log(stats._generation + ", " + stats._maxFitness + ", " + stats._meanFitness + ", " + stats._totalEvaluationCount + ", " + stats._maxComplexity);
//}
//NeatAlgorithmStats stats = ea.Statistics;
//Debug.Log(stats._generation+", "+stats._maxFitness+", "+stats._meanFitness+", "+stats._totalEvaluationCount+", "+stats._maxComplexity);
IGenomeDecoder <NeatGenome, IBlackBox> decoder = experiment.CreateGenomeDecoder();
IBlackBox box = decoder.Decode(ea.CurrentChampGenome);
FastAcyclicNetwork concrete = (FastAcyclicNetwork)box;
Debug.Log("Num hidden nodes = " + concrete.hiddenNodeCount + ", num connections = " + concrete.connectionCount);
box.InputSignalArray[0] = 0;
box.InputSignalArray[1] = 0;
box.Activate();
for (int i = 0; i < box.OutputCount; i++)
{
Debug.LogWarning("Output[" + i + "] = " + box.OutputSignalArray[i]);
}
}
