public void SetUp()
{
server = new HandServer();
Random r = new Random();
cache = new CachedHand(6, r);
}
public void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
var count = pop.GenomeList.Count;
#region Reset the genomes
for (var i = 0; i < count; i++)
{
pop.GenomeList[i].TotalFitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
pop.GenomeList[i].EvaluationCount = 0;
pop.GenomeList[i].Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
#endregion
//TODO: Parallelize/Distribute this loop
//Ideally we should have a distributed method which returns an array of
//doubles to add to the genome fitnesses of each individual.
for (var i = 0; i < count; i++)
{
Console.WriteLine("Individual #{0}", i + 1);
var g = pop.GenomeList[i];
var network = g.Decode(ActivationFunction);
if (network == null)
{
// Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
continue;
}
HandEngine engine = new HandEngine();
//Run multiple hands per individual
for (var curGame = 0; curGame < GamesPerEvaluation; curGame++)
{
#region Setup the players for this game
var field = new List <Seat>();
var stacks = GetStacks(PlayersPerGame);
var networks = new int[PlayersPerGame];
networks[0] = i;
IPlayer hero = null;//new NeuralNetworkPlayer(InputGenerator, OutputInterpreter,
// network, Rand);
field.Add(new Seat(1, "Net_" + i, stacks[0], hero));
for (var curPlayer = 1; curPlayer < PlayersPerGame; curPlayer++)
{
INetwork nextNetwork = null;
var next = 0;
while (nextNetwork == null)
{
next = Rand.Next(0, count);
nextNetwork = pop.GenomeList[next].Decode(ActivationFunction);
}
networks[curPlayer] = next;
//"NeuralNet" + next, stacks[curPlayer],
IPlayer villain = null; // new NeuralNetworkPlayer(InputGenerator,
// OutputInterpreter, nextNetwork, Rand);
field.Add(new Seat(curPlayer + 1, "Net" + next + "_Seat+ " + (curPlayer + 1), stacks[curPlayer], villain));
}
#endregion
//Have the players play a single hand.
HandHistory history = new HandHistory(field.ToArray(), (ulong)curGame + 1, (uint)(curGame % PlayersPerGame + 1),
new double[] { 1, 2 }, 0, BettingType);
CachedHand hand = CachedHands[Rand.Next(CachedHands.Count)];
engine.PlayHand(history);
#region Add the results to the players' fitness scores
//We'll use the profit as the fitness function.
//Alternatively, we could in the future experiment with using profit
//as a percentage of the original stacks. Or we could use the square
//of the profit (multiplying by -1 if the player lost money).
for (var curResult = 0; curResult < PlayersPerGame; curResult++)
{
var curGenome = pop.GenomeList[networks[curResult]];
curGenome.TotalFitness += field[curResult].Chips - stacks[curResult];
curGenome.EvaluationCount++;
}
#endregion
if (GamesPlayed % 10000 == 0)
{
using (TextWriter writer = new StreamWriter("game_" + GamesPlayed + ".txt"))
writer.WriteLine(history.ToString());
}
//increment the game counter
GamesPlayed++;
}
}
//Normalize the fitness scores to use the win-rate
for (var i = 0; i < count; i++)
{
pop.GenomeList[i].Fitness = Math.Max(pop.GenomeList[i].Fitness,
EvolutionAlgorithm.MIN_GENOME_FITNESS);
pop.GenomeList[i].TotalFitness = Math.Max(pop.GenomeList[i].Fitness,
EvolutionAlgorithm.MIN_GENOME_FITNESS);
}
}
