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**************");
}
/// <summary>
/// Evaluate the provided phenome and return its fitness score.
/// This takes a blackbox (pretty much the decoded neuralnetwork (the phenome)) that input can be passed to
/// and output processed in the domain. Makes it very easy, we dont have to wory about the genome (genelist) or phenome (network) at all
/// </summary>
public FitnessInfo Evaluate(IBlackBox box)
{
// Set a maximum number of evaluations
_evalCount++;
double fitness = 0.5;
FitnessInfo fitnessInfo = new FitnessInfo(fitness, fitness);
if (_evalCount > _nextEvalStop)
{
_nextEvalStop += _evalBlockSize;
_stopConditionSatisfied = true;
}
// Only use player model if it has been initialized. Otherwise, all fitness is 0.5
if (_experiment.playerModel != null)
{
// Get highlevel features
PCGSharpHighLevelFeatures featureCounts = new PCGSharpHighLevelFeatures();
PCGSharpHighLevelFeatures.C_HL_ROOMTYPE lastRoomType = PCGSharpHighLevelFeatures.C_HL_ROOMTYPE.NoPreviousRoom;
for (int i = 0; i < _experiment.geomNodeList.Count; i++)
{
// Get cppn output for each node
box.InputSignalArray[0] = _experiment.geomNodeList[i].normDepth;
box.InputSignalArray[1] = _experiment.geomNodeList[i].normSiblingIndex;
box.ResetState();
box.Activate();
double[] outputs = new double[box.OutputCount];
for (int j = 0; j < box.OutputCount; j++)
{
outputs[j] = box.OutputSignalArray[j];
}
// 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
double[] distributions = _experiment.playerModel.ClassifyNewData(featureCounts.ToDoubleArray());
// Fitness is the membership to the "Like" class (positive class)
fitnessInfo = new FitnessInfo(distributions[1], distributions[1]);
}
return(fitnessInfo);
}
public void TestSharpHighLevelFeatures()
{
IGenomeDecoder <NeatGenome, IBlackBox> decoder = experiment.CreateGenomeDecoder();
IBlackBox box = decoder.Decode(contentEA.CurrentChampGenome);
PCGSharpHighLevelFeatures featureCounts = new PCGSharpHighLevelFeatures();
for (int i = 0; i < ((PCGNeatExperiment)experiment).geomNodeList.Count; i++)
{
// Get cppn output for each node
PCGNeatNodeData currentNode = ((PCGNeatExperiment)experiment).geomNodeList[i];
box.InputSignalArray[0] = currentNode.normDepth;
box.InputSignalArray[1] = currentNode.normSiblingIndex;
box.ResetState();
box.Activate();
double[] outputs = new double[box.OutputCount];
for (int j = 0; j < box.OutputCount; j++)
{
outputs[j] = box.OutputSignalArray[j];
}
// Convert each nodes cppn output into a contentId
int combinedContentID = featureCounts.CPPNOutputToSettingsId(outputs);
// Get high level features from contentID and lastRoomType
PCGSharpHighLevelFeatures.C_HL_ROOMTYPE lastRoomType = PCGSharpHighLevelFeatures.C_HL_ROOMTYPE.NoPreviousRoom;
if (i > 0) // Only the first room should hav NoPreviousRomm
{
lastRoomType = currentNode.parentNode.roomType;
}
currentNode.roomType = featureCounts.UpdateFeatures(combinedContentID, lastRoomType);
}
featureCounts.PrintFeatures();
}
public void TestSharpHighLevelFeatures()
{
IGenomeDecoder<NeatGenome, IBlackBox> decoder = experiment.CreateGenomeDecoder();
IBlackBox box = decoder.Decode(contentEA.CurrentChampGenome);
PCGSharpHighLevelFeatures featureCounts = new PCGSharpHighLevelFeatures();
for (int i = 0; i < ((PCGNeatExperiment)experiment).geomNodeList.Count; i++) {
// Get cppn output for each node
PCGNeatNodeData currentNode = ((PCGNeatExperiment)experiment).geomNodeList[i];
box.InputSignalArray[0] = currentNode.normDepth;
box.InputSignalArray[1] = currentNode.normSiblingIndex;
box.ResetState();
box.Activate();
double[] outputs = new double[box.OutputCount];
for (int j = 0; j < box.OutputCount; j++) {
outputs[j] = box.OutputSignalArray[j];
}
// Convert each nodes cppn output into a contentId
int combinedContentID = featureCounts.CPPNOutputToSettingsId(outputs);
// Get high level features from contentID and lastRoomType
PCGSharpHighLevelFeatures.C_HL_ROOMTYPE lastRoomType = PCGSharpHighLevelFeatures.C_HL_ROOMTYPE.NoPreviousRoom;
if (i>0) // Only the first room should hav NoPreviousRomm
lastRoomType = currentNode.parentNode.roomType;
currentNode.roomType = featureCounts.UpdateFeatures(combinedContentID, lastRoomType);
}
featureCounts.PrintFeatures();
}
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**************");
}
/// <summary>
/// Evaluate the provided phenome and return its fitness score.
/// This takes a blackbox (pretty much the decoded neuralnetwork (the phenome)) that input can be passed to
/// and output processed in the domain. Makes it very easy, we dont have to wory about the genome (genelist) or phenome (network) at all
/// </summary>
public FitnessInfo Evaluate(IBlackBox box)
{
// Set a maximum number of evaluations
_evalCount++;
double fitness = 0;
FitnessInfo fitnessInfo = new FitnessInfo(fitness, fitness);
if (_evalCount > _nextEvalStop)
{
_nextEvalStop += _evalBlockSize;
Debug.Log("Eval Count = " + _evalCount);
if (_evalCount >= 500000)
{
_stopConditionSatisfied = true;
}
}
// Only use player model if it has been initialized. Otherwise, all fitness is 0.5
if (_experiment.geomNodeList != null)
{
// Get highlevel features
PCGSharpHighLevelFeatures featureCounts = new PCGSharpHighLevelFeatures();
PCGSharpHighLevelFeatures.C_HL_ROOMTYPE lastRoomType = PCGSharpHighLevelFeatures.C_HL_ROOMTYPE.NoPreviousRoom;
if (_experiment == null)
{
Debug.Log("Experiment null");
}
if (_experiment.geomNodeList == null)
{
Debug.Log("Geom node list null");
}
for (int i = 0; i < _experiment.geomNodeList.Count; i++)
{
// Get cppn output for each node
box.InputSignalArray[0] = _experiment.geomNodeList[i].normDepth;
box.InputSignalArray[1] = _experiment.geomNodeList[i].normSiblingIndex;
box.ResetState();
box.Activate();
double[] outputs = new double[box.OutputCount];
for (int j = 0; j < box.OutputCount; j++)
{
outputs[j] = box.OutputSignalArray[j];
}
// Convert each nodes cppn output into a contentId
int combinedContentID = featureCounts.CPPNOutputToSettingsId(outputs);
lastRoomType = featureCounts.UpdateFeatures(combinedContentID, lastRoomType);
}
// Compare to the features of existing map
double difference = featureCounts.CompareToOther(_originalFeatures);
if (difference == 0)
{
_stopConditionSatisfied = true;
}
difference = 1000 + difference;
// Fitness is the membership to the "Like" class (positive class)
fitnessInfo = new FitnessInfo(difference, difference);
}
else
{
_stopConditionSatisfied = true;
}
return(fitnessInfo);
}
/// <summary>
/// Evaluate the provided phenome and return its fitness score.
/// This takes a blackbox (pretty much the decoded neuralnetwork (the phenome)) that input can be passed to
/// and output processed in the domain. Makes it very easy, we dont have to wory about the genome (genelist) or phenome (network) at all
/// </summary>
public FitnessInfo Evaluate(IBlackBox box)
{
// Set a maximum number of evaluations
_evalCount++;
int difference = 0;
FitnessInfo fitnessInfo = new FitnessInfo(0.0, 0.0);
if (_evalCount > _nextEvalStop)
{
_nextEvalStop += _evalBlockSize;
Debug.Log("Eval Count = " + _evalCount);
if (_evalCount >= 500000)
{
_stopConditionSatisfied = true;
}
}
// Only use player model if it has been initialized. Otherwise, all fitness is 0.5
if (_experiment.geomNodeList != null)
{
// Get highlevel features
if (_experiment == null)
{
Debug.Log("Experiment null");
}
if (_experiment.geomNodeList == null)
{
Debug.Log("Geom node list null");
}
for (int i = 0; i < _experiment.geomNodeList.Count; i++)
{
// Get cppn output for each node
box.InputSignalArray[0] = _experiment.geomNodeList[i].normDepth;
box.InputSignalArray[1] = _experiment.geomNodeList[i].normSiblingIndex;
box.ResetState();
box.Activate();
double[] outputs = new double[box.OutputCount];
for (int j = 0; j < box.OutputCount; j++)
{
outputs[j] = box.OutputSignalArray[j];
}
// Convert each nodes cppn output into a contentId
int combinedContentID = featureCounts.CPPNOutputToSettingsId(outputs);
difference = difference + Math.Abs(_experiment.geomNodeList[i].contentSetting - combinedContentID);
}
// Compare to the features of existing map
if (difference == 0)
{
_stopConditionSatisfied = true;
}
difference = 100000 - difference;
// Fitness is the membership to the "Like" class (positive class)
fitnessInfo = new FitnessInfo((double)difference, (double)difference);
}
else
{
_stopConditionSatisfied = true;
}
return(fitnessInfo);
}
