private void Start()
{
entityManager = World.Active.EntityManager;
Entity earthEntityPrefab = GameObjectConversionUtility.ConvertGameObjectHierarchy(earthPrefab, World.Active);
Entity leafEntityPrefab = GameObjectConversionUtility.ConvertGameObjectHierarchy(leafPrefab, World.Active);
Entity antEntityPrefab = GameObjectConversionUtility.ConvertGameObjectHierarchy(wormPrefab, World.Active);
RandomSelection[] entityPrefabs = new RandomSelection[]
{
new RandomSelection(earthEntityPrefab, .3f),
new RandomSelection(leafEntityPrefab, .7f),
// new RandomSelection(antEntityPrefab, .1f)
};
float3 startPoint = new float3(-(width / 2f) * cellScale, -(height / 2f) * cellScale, 0);
//Create ants
int antHomeRaidus = 5;
int homeCenterX = UnityEngine.Random.Range(0, width);
int homeCenterY = UnityEngine.Random.Range(0, height);
for (int x = homeCenterX - antHomeRaidus; x < homeCenterX + antHomeRaidus; x++)
{
for (int y = homeCenterY - antHomeRaidus; y < homeCenterY + antHomeRaidus; y++)
{
if (Mathf.Sqrt(Mathf.Pow(x - homeCenterX, 2) + Mathf.Pow(y - homeCenterY, 2)) < antHomeRaidus)
{
float3 position = new float3(x * cellScale + startPoint.x, y * cellScale + startPoint.y, 0);
Entity antEntityinstance = entityManager.Instantiate(antEntityPrefab);
entityManager.SetComponentData(antEntityinstance, new GridPosition {
Value = new int2(x, y)
});
entityManager.SetComponentData(antEntityinstance, new Translation {
Value = position
});
}
}
}
//Generate grid
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
float3 position = new float3(x * cellScale + startPoint.x, y * cellScale + startPoint.y, 0);
Entity randomPrefab = GetRandomValue(entityPrefabs);
Entity instance = entityManager.Instantiate(randomPrefab);
entityManager.SetComponentData(instance, new GridPosition {
Value = new int2(x, y)
});
entityManager.SetComponentData(instance, new Translation {
Value = position
});
}
}
}
public void MudarOrdem() //Altera a ordem do surgimento das salas e dos Tiros
{
RandomSelection rs = gameObject.AddComponent <RandomSelection>() as RandomSelection; //Instanciar a classe "RandomSelection" em um objeto "RandomSelection" chamado rs
rs.ListaRandomSemRepeticao(RandomSelection.ordemSalas, 2); //Cria a ordem em que as salas irão aparecer
rs.ListaRandomSemRepeticao(RandomSelection.ordemTiros, 3); //Cria a ordem em que as tiros irão aparecer
}
private CultureObject GetCultureToSpawn()
{
List <IFaction> factionsAtWar = new List <IFaction>();
foreach (IFaction faction in Campaign.Current.Factions)
{
if (Hero.MainHero.Clan.IsAtWarWith(faction) && !faction.IsBanditFaction)
{
factionsAtWar.Add(faction);
}
}
if (factionsAtWar.Count == 0)
{
// The player isn't at war with anyone, we'll spawn bandits.
List <Settlement> hideouts = Settlement.FindAll((s) => { return(s.IsHideout()); }).ToList();
Settlement closestHideout = hideouts.MinBy((s) => { return(MobileParty.MainParty.GetPosition().DistanceSquared(s.GetPosition())); });
return(closestHideout.Culture);
}
else
{
// Pick one of the factions to spawn prisoners of
return(RandomSelection <IFaction> .GetRandomElement(factionsAtWar).Culture);
}
}
protected static void RandomSelection_LoadRandomness(RandomSelection __instance)
{
foreach (RandomListInfo info in RogueLibs.Instance.RandomLists)
{
__instance.CreateRandomList(info.name, info.category, info.objectType);
}
foreach (CustomItem item in RogueLibs.Instance.Items)
{
foreach (KeyValuePair <string, int> pair in item.SpawnDictionary)
{
RandomList list = null;
foreach (RandomList l in __instance.randomListTable.Values)
{
if (l.rName == pair.Key)
{
list = l;
break;
}
}
if (list != null)
{
__instance.CreateRandomElement(list, item.Id, pair.Value);
}
}
}
}
private void Start()
{
for (int i = 0; i < enemyColumns; i++)
{
RandomSelection randomSelection = new RandomSelection(i, enemyData.spawnBigProbability);
randomSelections.Add(randomSelection);
}
}
//Spin
public void requestSpin()
{
m_model.lastWinner = RandomSelection.SelectWinner(m_model.m_chancesToWin);
requestChangeCurrentCredits(-1 * m_model.CurrentBetAmount);
calculateImagesForLastSpin();
calculatePayoutForLastSpin();
}
private void calculateImagesForLastSpin()
{
switch (m_model.lastWinner)
{
case 0:
//no winner - no 3 of kind
//Iffy way of doing this
for (int i = 0; i < 3; i++)
{
switch (RandomSelection.GetRandomInt(1, 5))
{
case 1:
m_model.m_imagesToDisplay[i] = m_model.m_slotImages.ImageOne;
break;
case 2:
m_model.m_imagesToDisplay[i] = m_model.m_slotImages.ImageTwo;
break;
case 3:
m_model.m_imagesToDisplay[i] = m_model.m_slotImages.ImageThree;
break;
case 4:
m_model.m_imagesToDisplay[i] = m_model.m_slotImages.ImageFour;
break;
}
//All 3 match, reroll the last one. Should be ok to compare since they are references
if (m_model.m_imagesToDisplay[0].Equals(m_model.m_imagesToDisplay[1]) && m_model.m_imagesToDisplay[0].Equals(m_model.m_imagesToDisplay[2]))
{
i--;
}
}
break;
case 1:
//Image 1 won - 3 of kind
m_model.m_imagesToDisplay[0] = m_model.m_slotImages.ImageOne;
m_model.m_imagesToDisplay[1] = m_model.m_slotImages.ImageOne;
m_model.m_imagesToDisplay[2] = m_model.m_slotImages.ImageOne;
break;
case 2:
//Image 2 won - 3 of kind
m_model.m_imagesToDisplay[0] = m_model.m_slotImages.ImageTwo;
m_model.m_imagesToDisplay[1] = m_model.m_slotImages.ImageTwo;
m_model.m_imagesToDisplay[2] = m_model.m_slotImages.ImageTwo;
break;
case 3:
//Image 3 won - 3 of kind
m_model.m_imagesToDisplay[0] = m_model.m_slotImages.ImageThree;
m_model.m_imagesToDisplay[1] = m_model.m_slotImages.ImageThree;
m_model.m_imagesToDisplay[2] = m_model.m_slotImages.ImageThree;
break;
case 4:
//Image 4 won - 3 of kind
m_model.m_imagesToDisplay[0] = m_model.m_slotImages.ImageFour;
m_model.m_imagesToDisplay[1] = m_model.m_slotImages.ImageFour;
m_model.m_imagesToDisplay[2] = m_model.m_slotImages.ImageFour;
break;
}
}
public EvaluationTrain(GettingStarted gettingStarted)
{
_randomSelection = new RandomSelection(gettingStarted.EarlyPopulation);
_rouletteWeelSelection = new RouletteWeel2Selection();
_crossOver = new CrossOver(gettingStarted.Items, gettingStarted.KnapsakCapacity);
}
public override int GeneticAlgorithm(MainGraph graph, ProblemData problemData)
{
algorithmInfo = new AlgorithmInfo();
adjacencyList = AdjacencyList.GenerateList(graph);
cost = Cost.GanerateCostArray(graph, problemData); problem = problemData;
var crossover = GeneticMethod.ChosenCrossoverMethod(crossoverMethod, CrossoverProbability, dotCrossover);
var mutation = GeneticMethod.ChosenMutationMethod(mutationMethod, MutationProbability, dotMutation);
var selection = GeneticMethod.ChosenSelectionMethod(selectionMethod, CountTour, CountSelected);
ReductionForClassicGA reduction = new ReductionForClassicGA();
Chromosome bestChromosome = null;
Population startPopulation = new Population(PopulationSize, cost);
var population = startPopulation;
int stepGA = 0;
double MediumFitness = Solution.MediumFitnessPopulation(population);
RandomSelection randomSelection = new RandomSelection();
FitnessCalculation(population.populationList);
stopwatch = new Stopwatch();
stopwatch.Start();
while (stepGA < IterateSize)
{
List <Chromosome> midPopulation = selection.Selection(population);
List <Chromosome> parentPopulation = randomSelection.Selection(midPopulation, selection.indexSelectChrom);
List <Chromosome> childList = crossover.Crossover(parentPopulation);
if (childList.Count == 0)
{
continue;
}
mutation.Mutation(childList);
FitnessCalculation(childList);
reduction.Reduction(childList, randomSelection.indexTwoParant, population.populationList);
double tempMediumFitness = Solution.MediumFitnessPopulation(population);
double absFitness = Math.Abs(tempMediumFitness - MediumFitness);
MediumFitness = tempMediumFitness;
if (absFitness >= 0 && absFitness <= 1)
{
bestChromosome = population.BestChromosome();
var worstChromosome = population.WorstChromosome();
if (bestChromosome.fitness - worstChromosome.fitness <= 1 && bestChromosome.fitness - worstChromosome.fitness >= 0)
{
if (Solution.isAnswerTrue(bestChromosome, cost, problemData))
{
stopwatch.Stop();
algorithmInfo.Time = stopwatch.Elapsed;
algorithmInfo.BestFx = bestChromosome.fitness;
algorithmInfo.Steps = stepGA;
break;
}
}
}
stepGA++;
}
stopwatch.Stop();
if (stepGA == IterateSize)
{
bool answer = false;
bestChromosome = population.BestChromosome();
while (population.populationList.Count != 0)
{
if (Solution.isAnswerTrue(bestChromosome, cost, problemData))
{
algorithmInfo.Time = stopwatch.Elapsed;
algorithmInfo.BestFx = bestChromosome.fitness;
algorithmInfo.Steps = stepGA;
answer = true;
break;
}
else
{
population.populationList.Remove(bestChromosome);
if (population.populationList.Count == 0)
{
break;
}
bestChromosome = population.BestChromosome();
}
}
if (!answer)
{
bestChromosome = null;
}
}
return(Solution.Answer(cost, bestChromosome, problemData, graph));
}
/// <summary>
/// Реализация генетического алгоритма "Genitor".
/// </summary>
/// <param name="graph">Граф.</param>
/// <param name="problemData">Параметры задачи</param>
public override int GeneticAlgorithm(MainGraph graph, ProblemData problemData)
{
this.algorithmInfo = new AlgorithmInfo();
// Инициализация основных структур.
adjacencyList = AdjacencyList.GenerateList(graph);
cost = Cost.GanerateCostArray(graph, problemData); ProblemData problem = problemData;
var crossover = GeneticMethod.ChosenCrossoverMethod(crossoverMethod, 100, dotCrossover);
var mutation = GeneticMethod.ChosenMutationMethod(mutationMethod, MutationProbability, dotMutation);
var selection = GeneticMethod.ChosenSelectionMethod(selectionMethod, CountTour, CountSelected);
Chromosome bestChromosome = null;
double MediumFitness = 0;
Population startPopulation = new Population(PopulationSize, cost);
var population = startPopulation;
int stepGA = 0;
// вычесление пригодности хромосом.
for (int i = 0; i < PopulationSize; i++)
{
population.populationList[i].fitness = Fitness.FunctionTrue(cost, problemData, population.populationList[i]);
}
RandomSelection randomSelection = new RandomSelection();
MediumFitness = Solution.MediumFitnessPopulation(population);
stopwatch = new Stopwatch();
stopwatch.Start();
while (stepGA < IterateSize)
{
List <Chromosome> midPopulation = selection.Selection(population);
List <Chromosome> parentPopulation = randomSelection.Selection(midPopulation);
Chromosome child = crossover.Crossover(parentPopulation[0], parentPopulation[1]);
if (mutation != null)
{
mutation.Mutation(child);
}
// вычесление ранга хромосомы.
//population.Sort();
Ranking2(population);
// пригодность потомка
child.fitness = Fitness.FunctionTrue(cost, problemData, child);
// поиск самой худщей хромосомы
Chromosome bedChrom = null;
bedChrom = population.populationList.First();
// замена худшей хромосомы на потомка
int index = population.populationList.IndexOf(bedChrom);
population.populationList.Insert(index, child);
population.populationList.RemoveAt(index + 1);
double tempMediumFitness = Solution.MediumFitnessPopulation(population);
double absFitness = Math.Abs(tempMediumFitness - MediumFitness);
MediumFitness = tempMediumFitness;
if (absFitness >= 0 && absFitness <= 1)
{
bestChromosome = population.BestChromosome();
var worstChromosome = population.WorstChromosome();
if (bestChromosome.fitness - worstChromosome.fitness <= 1 && bestChromosome.fitness - worstChromosome.fitness >= 0)
{
if (Solution.isAnswerTrue(bestChromosome, cost, problemData))
{
stopwatch.Stop();
algorithmInfo.Time = stopwatch.Elapsed;
algorithmInfo.BestFx = bestChromosome.fitness;
algorithmInfo.Steps = stepGA;
break;
}
}
}
stepGA++;
}
stopwatch.Stop();
if (stepGA == IterateSize)
{
bool answer = false;
bestChromosome = population.BestChromosome();
while (population.populationList.Count != 0)
{
if (Solution.isAnswerTrue(bestChromosome, cost, problemData))
{
algorithmInfo.Time = stopwatch.Elapsed;
algorithmInfo.BestFx = bestChromosome.fitness;
algorithmInfo.Steps = stepGA;
answer = true;
break;
}
else
{
population.populationList.Remove(bestChromosome);
if (population.populationList.Count == 0)
{
break;
}
bestChromosome = population.BestChromosome();
}
}
if (!answer)
{
bestChromosome = null;
}
}
return(Solution.Answer(cost, bestChromosome, problemData, graph));
}
