public JPSStrategy(FMap fMap, FMap gMap, string id) : base(fMap, id)
{
openSet = new List <Point>();
parent = new Dictionary <Point, Point>();
this.gMap = gMap;
}
public GAStrategy(FMap fMap, string id, GAOpts opt) : base(fMap, id)
{
rng = new Random(Guid.NewGuid().GetHashCode());
popSize = opt.popSize; // population size
mutRate = opt.mutRate; // mutation rate
fitnessMulti = opt.fitMulti; //fitness multiplier
ValueDiversity = opt.diversity; //whether we value candidate diversity or not
Elite = opt.elite; //Mix in the genes of the best path found when creating next generations
deepCount = 1;
deepeningInc = opt.deepeningInc;
parents = new Individual[Math.Max((int)(popSize * 0.1), 2)]; //parents as a fifth of the population
dnaPool = new List <MasterGene>(); //action counter for crossovers
BestDNA = new List <MoveDir>();
//initialise generation
generation = new Individual[popSize];
for (int i = 0; i < popSize; i++)
{
generation[i] = new Individual(fMap.Start);
}
started = false;
}
protected SearchStrategy(FMap fMap, string id)
{
this.id = id;
this.fMap = fMap;
closedSet = new Dictionary <Point, bool>();
for (int i = 0; i < fMap.Width; i++)
{
for (int j = 0; j < fMap.Height; j++)
{
closedSet[new Point(i, j)] = false;
}
}
Path = new List <Point>();
paused = true;
gridW = SwinGame.ScreenWidth() / fMap.Width;
gridH = SwinGame.ScreenHeight() - 200 / fMap.Height;
gridW = gridH = gridW < gridH ? gridW : gridH;
sw = new Stopwatch();
sw.Reset();
}
public BFSStrategy(FMap fMap, string id) : base(fMap, id)
{
openSet = new List <Point>();
parent = new Dictionary <Point, Point>();
}
public DFSStrategy(FMap fMap, string id) : base(fMap, id)
{
stack = new Stack <Point>();
}
