public void CanGenerateGenoType()
{
var randomGenerator = new UniformRandomGenerator();
var possibleFunctions = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus()
};
var possibleTerminals = new List <IGenoTypeNode>
{
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var eaGeneExpressionParameters = new EaGeneExpressionParameters(4, possibleFunctions, possibleTerminals);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
var genoType = genoTypeFactory.GetGenoType();
Assert.NotNull(genoType);
}
public UniformRandomSequenceGenerator(uint dimensionality, UniformRandomGenerator rng) : this(NQuantLibcPINVOKE.new_UniformRandomSequenceGenerator(dimensionality, UniformRandomGenerator.getCPtr(rng)), true)
{
if (NQuantLibcPINVOKE.SWIGPendingException.Pending)
{
throw NQuantLibcPINVOKE.SWIGPendingException.Retrieve();
}
}
public GaussianRandomGenerator(UniformRandomGenerator rng) : this(NQuantLibcPINVOKE.new_GaussianRandomGenerator(UniformRandomGenerator.getCPtr(rng)), true)
{
if (NQuantLibcPINVOKE.SWIGPendingException.Pending)
{
throw NQuantLibcPINVOKE.SWIGPendingException.Retrieve();
}
}
public void todo(IDataSet dataSet)
{
var randomGenerator = new UniformRandomGenerator();
var possibleFunctions = new List <IGenoTypeNode>
{
new Multiplication(),
new Plus(),
new Minus()
};
var possibleTerminals = new List <IGenoTypeNode>();
foreach (var mappedColumn in dataSet.MappedColumns)
{
possibleTerminals.Add(new FeatureTerminal(mappedColumn.Key));
}
var eaGeneExpressionParameters = new EaGeneExpressionParameters(10, possibleFunctions, possibleTerminals);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
var genoType = genoTypeFactory.GetGenoType();
var phenoTypeTree = new PhenoTypeTree(genoType.GenoTypeNodes);
var stringExpresssion = phenoTypeTree.ToString();
// TODO make mapped columns more sophisticated
var mappedColumnsUsedInExpression = new Dictionary <string, int>();
foreach (var mappedColumn in dataSet.MappedColumns)
{
if (stringExpresssion.Contains(mappedColumn.Key))
{
mappedColumnsUsedInExpression.Add(mappedColumn.Key, mappedColumn.Value);
}
}
var expression = new Expression(stringExpresssion);
var numberOfRows = dataSet.MappedData.GetLength(0);
var sum = 0.0;
for (var row = 0; row < numberOfRows; row++)
{
foreach (var usedMappedColumn in mappedColumnsUsedInExpression)
{
expression.Parameters[usedMappedColumn.Key.Replace("]", "").Replace("[", "")] = dataSet.MappedData[row, usedMappedColumn.Value];
}
if (!expression.HasErrors())
{
var test = (double)expression.Evaluate();
sum = sum + test;
}
}
}
void Initialize(int regionSize, int gaussianMin, int gaussianMax, bool loading = false)
{
CreatureList = creatureList.AsReadOnly();
EntityList = entityList.AsReadOnly();
CreateRegions(regionSize, loading);
Random = new UniformRandomGenerator();
GaussianRandom = new NormalRandomGenerator(gaussianMin, gaussianMax, Random);
}
void Initialize(int regionSize, UniformRandomGenerator random, NormalRandomGenerator gaussianRandom, bool loading = false)
{
CreatureList = creatureList.AsReadOnly();
EntityList = entityList.AsReadOnly();
CreateRegions(regionSize, loading);
Random = random;
GaussianRandom = gaussianRandom;
}
public IGenoTypeNode GetTerminalNode()
{
if (UniformRandomGenerator.GetContinousRandomNumber(0, 1.0) <
EaGeneExpressionParameters.ParameterProbability)
{
return(ParameterTerminalFactory.GetParameterTerminal());
}
return(EaGeneExpressionParameters.PossibleTerminals[
UniformRandomGenerator.GetIntegerRandomNumber(0, NumberOfPossibleTerminals)]);
}
public RandomNumbersGenerator()
{
//random = new Random();
arrivalTimeUniformRandom = new UniformRandomGenerator();
regularDrugsServiceTimeNormalRandom = new NormalRandomGenerator(600, 1300);
regularDrugsServiceTimeNormalRandom.Mean = 900;
hardDrugsServiceTimeNormalRandom = new NormalRandomGenerator(900, 1830);
hardDrugsServiceTimeNormalRandom.Mean = 1400;
typeOfRecipeNormalRandom = new NormalRandomGenerator(0, 10);
typeOfRecipeNormalRandom.Mean = 3;
}
public IGenoTypeNode GetParameterTerminal()
{
if (UniformRandomGenerator.GetContinousRandomNumber(0, 1.0) <=
EaGeneExpressionParameters.ConstantProbability)
{
return(new ParameterTerminal(EaGeneExpressionParameters.PossibleConstants[UniformRandomGenerator.GetIntegerRandomNumber(0, PossibleConstantsCountMinusOne)].Value));
}
return(EaGeneExpressionParameters.ParameterTypeInteger
? new ParameterTerminal(UniformRandomGenerator.GetIntegerRandomNumber((int)EaGeneExpressionParameters.ParameterMinimum, (int)EaGeneExpressionParameters.ParameterMaximum))
: new ParameterTerminal(UniformRandomGenerator.GetContinousRandomNumber(EaGeneExpressionParameters.ParameterMinimum, EaGeneExpressionParameters.ParameterMaximum)));
}
public void PerformCrossover(ref IGenoType genoTypeLeft, ref IGenoType genoTypeRight)
{
if ((new PhenoTypeTree(genoTypeLeft.GenoTypeNodes)).ToString().Equals((new PhenoTypeTree(genoTypeRight.GenoTypeNodes)).ToString()))
{
return;
}
// var left1 = new PhenoTypeTree(genoTypeLeft.GenoTypeNodes).ToString();
// var right1 = new PhenoTypeTree(genoTypeRight.GenoTypeNodes).ToString();
if (!(UniformRandomGenerator.GetContinousRandomNumber(0, 1.0) <= EaGeneExpressionParameters.CrossoverProbability))
{
return;
}
if (UniformRandomGenerator.GetContinousRandomNumber(0, 1.0) < 0.5)
{
var numberOfNodes = PhenoTypeTree.GetNumberOfNodes(genoTypeLeft.GenoTypeNodes);
var crossOverPosition1 = UniformRandomGenerator.GetIntegerRandomNumber(0, numberOfNodes - 1);
if (UniformRandomGenerator.GetContinousRandomNumber(0, 1.0) < 0.1)
{
for (var index = crossOverPosition1; index < genoTypeLeft.GenoTypeNodes.Count; index++)
{
Swap(genoTypeLeft.GenoTypeNodes, genoTypeRight.GenoTypeNodes, index);
}
}
else
{
for (var index = 0; index <= crossOverPosition1; index++)
{
Swap(genoTypeLeft.GenoTypeNodes, genoTypeRight.GenoTypeNodes, index);
}
}
}
else
{
var crossOverPosition1 = UniformRandomGenerator.GetIntegerRandomNumber(1, genoTypeLeft.GenoTypeNodes.Count - 2);
var crossOverPosition2 = UniformRandomGenerator.GetIntegerRandomNumber(crossOverPosition1, genoTypeLeft.GenoTypeNodes.Count - 2);
for (var index = crossOverPosition1; index < crossOverPosition2; index++)
{
Swap(genoTypeLeft.GenoTypeNodes, genoTypeRight.GenoTypeNodes, index);
}
}
// var left2 = new PhenoTypeTree(genoTypeLeft.GenoTypeNodes).ToString();
// var right2 = new PhenoTypeTree(genoTypeRight.GenoTypeNodes).ToString();
}
public void PerformMutation(ref IGenoType genoType)
{
if (!(UniformRandomGenerator.GetContinousRandomNumber(0, 1.0) <= EaGeneExpressionParameters.MutationProbability))
{
return;
}
var randomNumber = UniformRandomGenerator.GetContinousRandomNumber(0, 1.0);
if (randomNumber <= 0.3)
{
genoType = GenoTypeFactory.GetGenoType();
}
else
{
//var index = UniformRandomGenerator.GetIntegerRandomNumber(0, genoType.GenoTypeNodes.Count - 1);
var numberOfNodes = PhenoTypeTree.GetNumberOfNodes(genoType.GenoTypeNodes);
var index = UniformRandomGenerator.GetIntegerRandomNumber(0, numberOfNodes - 1);
if (randomNumber <= 0.6)
{
var node = genoType.GenoTypeNodes[index];
genoType.GenoTypeNodes.RemoveAt(index);
index = UniformRandomGenerator.GetIntegerRandomNumber(0, genoType.GenoTypeNodes.Count - 1);
genoType.GenoTypeNodes.Insert(index, node);
}
else
{
if (genoType.GenoTypeNodes[index].GetType() == typeof(ParameterTerminal))
{
((ParameterTerminal)genoType.GenoTypeNodes[index]).Value =
((ParameterTerminal)genoType.GenoTypeNodes[index]).Value +
UniformRandomGenerator.GetContinousRandomNumber(-3.0, 3.0);
}
else
{
if (index < EaGeneExpressionParameters.MaximumNumberOfHeadNodes)
{
genoType.GenoTypeNodes[index] = GenoTypeFactory.GetFunctionOrTerminalNode();
}
else
{
genoType.GenoTypeNodes[index] = GenoTypeFactory.GetTerminalNode();
}
}
}
}
}
public void CanTranslateTest3()
{
var randomGenerator = new UniformRandomGenerator();
var possibleFunctions = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Division(),
new Plus(),
new Minus(),
new Minimum(),
new Maximum(),
new Not(),
new Exp(),
new Sinus(),
new Cosinus()
};
var possibleTerminals = new List <IGenoTypeNode>
{
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var eaGeneExpressionParameters = new EaGeneExpressionParameters(20, possibleFunctions, possibleTerminals);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
eaGeneExpressionParameters.ParameterTypeInteger = true;
eaGeneExpressionParameters.ConstantProbability = 0;
var genoType = genoTypeFactory.GetGenoType();
var phenoTypeTree = new GeneExpression.PhenoTypeTree(genoType.GenoTypeNodes);
var expresssion = phenoTypeTree.ToString();
}
public IGenoTypeNode GetFunctionNode()
{
return(EaGeneExpressionParameters.PossibleFunctions[
UniformRandomGenerator.GetIntegerRandomNumber(0, NumberOfPossibleFunctions)]);
}
public World(int regionSize, UniformRandomGenerator random, NormalRandomGenerator gaussianRandom)
: base("World")
{
SpawnPointList = new List <SpawnPoint>();
Initialize(regionSize, random, gaussianRandom);
}
public FoodWorld(int regionSize, UniformRandomGenerator random, NormalRandomGenerator gaussianRandom)
: base(regionSize, random, gaussianRandom)
{
FoodToClone = new Food();
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(UniformRandomGenerator obj) {
return (obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr;
}
public GaussianRandomGenerator(UniformRandomGenerator rng) : this(NQuantLibcPINVOKE.new_GaussianRandomGenerator(UniformRandomGenerator.getCPtr(rng)), true) {
if (NQuantLibcPINVOKE.SWIGPendingException.Pending) throw NQuantLibcPINVOKE.SWIGPendingException.Retrieve();
}
public void CanCrossover()
{
var listLeft = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus(),
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var listRight = new List <IGenoTypeNode>
{
new Sinus(),
new Multiplication(),
new Division(),
new Minus(),
new FeatureTerminal("a1"),
new FeatureTerminal("b1"),
new FeatureTerminal("c1"),
new FeatureTerminal("d1")
};
var possibleFunctions = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus()
};
var possibleTerminals = new List <IGenoTypeNode>
{
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var eaGeneExpressionParameters =
new EaGeneExpressionParameters(4, possibleFunctions, possibleTerminals)
{
CrossoverProbability = 1.0
};
IGenoType left = new GeneExpression.GenoType {
GenoTypeNodes = listLeft
};
IGenoType right = new GeneExpression.GenoType {
GenoTypeNodes = listRight
};
var randomGenerator = new UniformRandomGenerator();
var genotypeCrossoverator1 = new GenoTypeCrossoveratorBasic1(randomGenerator, eaGeneExpressionParameters);
var phenoTypeTreeLeft1 = new GeneExpression.PhenoTypeTree(left.GenoTypeNodes);
var phenoTypeTreeRight1 = new GeneExpression.PhenoTypeTree(right.GenoTypeNodes);
genotypeCrossoverator1.PerformCrossover(ref left, ref right);
var phenoTypeTreeLeft2 = new GeneExpression.PhenoTypeTree(left.GenoTypeNodes);
var phenoTypeTreeRight2 = new GeneExpression.PhenoTypeTree(right.GenoTypeNodes);
Assert.NotEqual(phenoTypeTreeLeft1, phenoTypeTreeLeft2);
Assert.NotEqual(phenoTypeTreeRight1, phenoTypeTreeRight2);
}
static void Main()
{
var randomGenerator = new UniformRandomGenerator();
var dataSet = GetDataSet();
var target = GetTarget();
var eaGeneExpressionParameters = GetEaGeneExpressionParameters(dataSet);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
var mutator = new GenoTypeMutatorBasic1(randomGenerator, eaGeneExpressionParameters, genoTypeFactory);
var crossOverator = new GenoTypeCrossoveratorBasic1(randomGenerator, eaGeneExpressionParameters);
var populationP = GetFirstPopulation(eaGeneExpressionParameters, parameterTerminalFactory, genoTypeFactory);
var listOfObjectiveValuesP = new List <IObjectiveValues>();
for (var c = 0; c < populationP.Count; c++)
{
var objectiveValues = GetObjectiveValues(target, dataSet, populationP[c]);
listOfObjectiveValuesP.Add(objectiveValues);
}
listOfObjectiveValuesP = Nsga2TournamentSelector.PerformSelection(eaGeneExpressionParameters.TournamentSize, listOfObjectiveValuesP, randomGenerator);
var tempPopulation = new List <Individual>();
foreach (var objectiveValues in listOfObjectiveValuesP)
{
var indy = populationP.FirstOrDefault(x => x.Guid == objectiveValues.IndividualGuid);
tempPopulation.Add((Individual)indy.Clone());
}
populationP = tempPopulation;
for (var generation = 0; generation < eaGeneExpressionParameters.NumberOfGeneration; generation++)
{
var populationQ = new List <Individual>();
foreach (var individual in populationP)
{
populationQ.Add((Individual)individual.Clone());
}
populationQ = PerformCrossOver(populationQ, randomGenerator, crossOverator);
foreach (var individual in populationQ)
{
mutator.PerformMutation(ref individual.GenoType);
}
var combinedPopulation = new List <Individual>();
var combinedlistOfObjectiveValues = new List <IObjectiveValues>();
foreach (var individual in populationP)
{
var clone = (Individual)individual.Clone();
combinedPopulation.Add(clone);
var objectiveValues = GetObjectiveValues(target, dataSet, clone);
combinedlistOfObjectiveValues.Add(objectiveValues);
}
foreach (var individual in populationQ)
{
var clone = (Individual)individual.Clone();
combinedPopulation.Add(clone);
var objectiveValues = GetObjectiveValues(target, dataSet, clone);
combinedlistOfObjectiveValues.Add(objectiveValues);
}
combinedlistOfObjectiveValues = Nsga2Ranker.Rank(combinedlistOfObjectiveValues);
combinedlistOfObjectiveValues = Nsga2Crowder.CalculateCrowdingDistances(combinedlistOfObjectiveValues);
combinedlistOfObjectiveValues = combinedlistOfObjectiveValues.OrderBy(i => i.Rank).ThenByDescending(i => i.CrowdingDistance).ToList();
tempPopulation = new List <Individual>();
var counter = 0;
var smallestMse = decimal.MaxValue;
decimal largestMse = 0;
var smallestPosition = -1;
foreach (var objectiveValues in combinedlistOfObjectiveValues)
{
var indy = combinedPopulation.FirstOrDefault(x => x.Guid == objectiveValues.IndividualGuid);
tempPopulation.Add((Individual)indy.Clone());
var ovs = GetObjectiveValues(target, dataSet, combinedPopulation.FirstOrDefault(x => x.Guid == objectiveValues.IndividualGuid));
if (ovs.Values[0] < smallestMse)
{
smallestMse = ovs.Values[0];
smallestPosition = counter;
}
if (ovs.Values[0] > largestMse)
{
largestMse = ovs.Values[0];
}
counter++;
if (counter == eaGeneExpressionParameters.PopulationSize)
{
break;
}
}
populationP = tempPopulation;
Console.WriteLine(generation + " " + smallestMse + " " + largestMse);
Console.WriteLine(new PhenoTypeTree(populationP[smallestPosition].GenoType.GenoTypeNodes));
}
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(UniformRandomGenerator obj)
{
return((obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr);
}
public IGenoTypeNode GetFunctionOrTerminalNode()
{
return(UniformRandomGenerator.GetContinousRandomNumber(0, 1.0) <
EaGeneExpressionParameters.FunctionProbability ? GetFunctionNode() : GetTerminalNode());
}
public override void Work(Microsoft.Xna.Framework.GameTime gameTime)
{
base.Work(gameTime);
int foodCount = 0;
foreach (Entity entity in EntityList)
{
if (entity.EntityName == "Food")
{
foodCount += 1;
}
}
if (foodCount < MinimumFood)
{
var rand = new UniformRandomGenerator();
for (int i = foodCount; i < MinimumFood; i++)
{
Food food = new Food();
AddEntity(food);
food.Position = new EntityPosition(rand.Next(Width), rand.Next(Height));
}
}
int creatureCount = 0;
foreach (Entity entity in EntityList)
{
if (entity.EntityName == "Creature")
{
creatureCount += 1;
}
}
//if (creatureCount < MinimumCreatures)
//{
// for (int i = creatureCount; i < MinimumCreatures; i++)
// {
// var creature1 = CreatureList[Pseudo.Random.Next(0, CreatureList.Count)];
// Creature creature2;
// do
// {
// creature2 = CreatureList[Pseudo.Random.Next(0, CreatureList.Count)];
// } while (creature1 == creature2);
// double posX = Pseudo.Random.Next(Width);
// double posY = Pseudo.Random.Next(Height);
// var child = creature1.CreateOffspring(creature2, posX, posY);
// child.Rotation = Math.PI * 2 * Pseudo.Random.NextDouble();
// child.Energy.SetToMax();
// AddEntity(child);
// }
//}
if (runTime)
{
newGenerationTick += 1;
}
if (newGenerationTick >= newGenerationPeriod)
{
newGenerationTick = 0;
spawnNewGeneration();
}
}
public UniformRandomSequenceGenerator(uint dimensionality, UniformRandomGenerator rng) : this(NQuantLibcPINVOKE.new_UniformRandomSequenceGenerator(dimensionality, UniformRandomGenerator.getCPtr(rng)), true) {
if (NQuantLibcPINVOKE.SWIGPendingException.Pending) throw NQuantLibcPINVOKE.SWIGPendingException.Retrieve();
}
public FitnessWorld(int regionSize, UniformRandomGenerator random, NormalRandomGenerator gaussianRandom)
: base(regionSize, random, gaussianRandom)
{
}
