public override long Evaluate(Genome g, DataDictionary dd)
{
Data2048Dictionary d2d = dd as Data2048Dictionary;
long best = long.MinValue;
long sum = 0;
dd.CreateScore(g.GenomeId, Execute);
dd.CreateLifetime(g.GenomeId, Execute);
int lifetime;
for (int i = 0; i < Execute; ++i)
{
LinkedList <CreatedCells> cellLog = new LinkedList <CreatedCells>();
Initialize(cellLog);
lifetime = 0;
while (!Gameover)
{
double[] output = g.EvaluateNetwork(GetInputs());
int[] order = new int[4] {
0, 1, 2, 3
};
Array.Sort(output, order);
int index = 3;
while (!Shift((Direction)order[index]))
{
if (++FailCount >= FailLimit)
{
Gameover = true;
break;
}
--index;
}
if (Gameover)
{
break;
}
CreateRandom(SizeEmpty(), cellLog);
SetGameover();
++lifetime;
}
sum += GetFitness();
if (GetFitness() > best)
{
best = GetFitness();
d2d.AddCells(g.GenomeId, cellLog);
}
dd.AddScore(g.GenomeId, Score, i);
}
return(sum / Execute);
}
// 순서대로 (1,0), (0,1), (1,1), (0,0)을 입력값에 넣었을 때,
// 정답이 '참'인 경우 출력값이 1보다 적으면 정답에서 출력값의 차를 제곱한 값과,
// 정답이 '거짓'인 경우 출력값이 -1보다 크면 정답에서 출력값의 차를 제곱한 값의 합을 계산하고, (정답이 참 또는 거짓이라는게 정답을 맞췄다는 의미가 아니다. xor 연산의 결과가 참 또는 거짓인지를 의미한다.)
// 그 값의 역수에 1000을 곱한 값을 적합도 함수로 사용하였다.
public override long Evaluate(Genome g, DataDictionary dd)
{
int score = 0;
double fitness = 0;
double data;
double val;
if ((data = g.EvaluateNetwork(new double[] { 1, 0 })[0]) > 0)
{
++score;
}
val = MathUtils.Sigmoid(data) - 1;
if (val > 0)
{
val = 0;
}
fitness += val * val;
if ((data = g.EvaluateNetwork(new double[] { 0, 1 })[0]) > 0)
{
++score;
}
val = MathUtils.Sigmoid(data) - 1;
if (val > 0)
{
val = 0;
}
fitness += val * val;
if ((data = g.EvaluateNetwork(new double[] { 1, 1 })[0]) < 0)
{
++score;
}
val = MathUtils.Sigmoid(data) + 1;
if (val < 0)
{
val = 0;
}
fitness += val * val;
if ((data = g.EvaluateNetwork(new double[] { 0, 0 })[0]) < 0)
{
++score;
}
val = MathUtils.Sigmoid(data) + 1;
if (val < 0)
{
val = 0;
}
fitness += val * val;
dd.CreateScore(g.GenomeId, 1);
dd.AddScore(g.GenomeId, score, 0);
return((long)(1 / fitness * 1000));
}
// 순서대로 (1,0), (0,1), (1,1), (0,0)을 입력값에 넣었을 때,
// (1,0)과 (0,1)에서는 참을 나타내는 0보다 큰 값,
// (0,0)과 (1,1)에서는 거짓을 나타내는 0보다 작은 값(이 기준은 임의로 잡은 것이다.)이 나오면 맞는 결과라 보고
// 맞춘 횟수를 적합도로 설정하였다.
public override long Evaluate(Genome g, DataDictionary dd)
{
int score = 0;
if (g.EvaluateNetwork(new double[] { 1, 0 })[0] > 0)
{
++score;
}
if (g.EvaluateNetwork(new double[] { 0, 1 })[0] > 0)
{
++score;
}
if (g.EvaluateNetwork(new double[] { 1, 1 })[0] < 0)
{
++score;
}
if (g.EvaluateNetwork(new double[] { 0, 0 })[0] < 0)
{
++score;
}
dd.CreateScore(g.GenomeId, 1);
dd.AddScore(g.GenomeId, score, 0);
return(score + 1);
}
public DataDictionary Read()
{
DataDictionary d = null;
StreamReader sr = new StreamReader(Filename);
string data;
while ((data = sr.ReadLine()) != null)
{
JObject j;
if (data.StartsWith("\"Settings\""))
{
j = JObject.Parse(data.Substring(11));
Execution = j["Execution"].ToObject <int>();
Population = j["Population"].ToObject <int>();
DeltaThreshold = j["DeltaThreshold"].ToObject <double>();
DeltaDisjoint = j["DeltaDisjoint"].ToObject <double>();
DeltaWeight = j["DeltaWeight"].ToObject <double>();
PerturbChance = j["PerturbChance"].ToObject <double>();
StepSize = j["StepSize"].ToObject <double>();
LinkMutationChance = j["LinkMutationChance"].ToObject <double>();
ConnectionMutationChance = j["ConnectionMutationChance"].ToObject <double>();
NodeMutationChance = j["NodeMutationChance"].ToObject <double>();
EnableMutationChance = j["EnableMutationChance"].ToObject <double>();
DisableMutationChance = j["DisableMutationChance"].ToObject <double>();
SurviveRate = j["SurviveRate"].ToObject <double>();
Staleness = j["Staleness"].ToObject <int>();
JToken inputToken = j["InputMode"];
if (inputToken == null)
{
InputMode = 0;
}
else
{
InputMode = inputToken.ToObject <int>();
}
JToken gametoken = j["Game"];
if (gametoken == null)
{
GameFactory = new SnakeGameFactory(16, 16);
pool = new Pool(24, 4, null);
}
else
{
string game = gametoken.ToString();
if (game.StartsWith("Snake"))
{
string[] ds = game.Split('_');
GameFactory = new SnakeGameFactory(int.Parse(ds[1]), int.Parse(ds[2]));
pool = new Pool(24, 4, null);
}
else if (game.Equals("2048"))
{
GameFactory = new Game2048Factory();
pool = new Pool(16, 4, null);
}
else if (game.Equals("Pacman"))
{
GameFactory = new PacmanGameFactory();
pool = new Pool(1, 4, null);
}
}
d = GameFactory.GetDataDictionary();
}
else if (data.StartsWith("\"Gen\""))
{
j = JObject.Parse(data.Substring(6));
Gen = j["gen"].ToObject <int>();
Genome g = new Genome(pool);
Best.Add(g);
g.GenomeId = j["BestGenome"]["Id"].ToObject <int>();
g.FromGeneration = j["BestGenome"]["From"].ToObject <int>();
g.Fitness = j["BestGenome"]["Fitness"].ToObject <long>();
d.CreateScore(g.GenomeId, 1);
d.AddScore(g.GenomeId, j["BestGenome"]["topScore"].ToObject <int>(), 0);
g.ExecutionTime = j["BestGenome"]["ExecutionTime"].ToObject <long>();
}
else if (data.StartsWith("\"Genome\""))
{
j = JObject.Parse(data.Substring(9));
var ja = j.SelectToken("Genes");
foreach (JObject item in ja)
{
Gene gene = new Gene(item["In"].ToObject <int>(), item["Out"].ToObject <int>(),
item["Weight"].ToObject <double>(), item["Enable"].ToObject <bool>(), item["Innovation"].ToObject <int>());
Best[Gen].Genes.Add(gene);
}
ja = j.SelectToken("food");
if (ja != null)
{
SnakeDataDictionary sdd = d as SnakeDataDictionary;
LinkedList <Square> foods = new LinkedList <Square>();
foreach (var food in ja)
{
foods.AddLast(new Square(food[0].ToObject <int>(), food[1].ToObject <int>()));
}
sdd.AddFood(Best[Gen].GenomeId, foods);
}
ja = j.SelectToken("cells");
if (ja != null)
{
Data2048Dictionary d2d = d as Data2048Dictionary;
LinkedList <CreatedCells> cc = new LinkedList <CreatedCells>();
foreach (var cells in ja)
{
cc.AddLast(new CreatedCells(cells[0].ToObject <int>(), cells[1].ToObject <int>() == 2));
}
d2d.AddCells(Best[Gen].GenomeId, cc);
}
}
else if (data.StartsWith("\"Species\""))
{
}
}
return(d);
}
