private static int calculateLabel(Individual[] weights)
{
Individual[] reversedWeights = { weights[1], weights[0] };
weights[1].ResetScores();
weights[0].ResetScores();
SmartThreadPool smtp = new SmartThreadPool();
for (int index = 0; index < numberOfGames; index++)
{
if (index % 2 == 1)
{
smtp.QueueWorkItem(new WorkItemCallback(threadProc), weights);
}
else
{
smtp.QueueWorkItem(new WorkItemCallback(threadProc), reversedWeights);
}
}
smtp.WaitForIdle();
smtp.Shutdown();
//double strengthRatio = ((double)weights[0].Wins + (double)weights[0].Draws / 2) / numberOfGames;
//return strengthRatio > 0.5 ? 1 : -1;
return weights[0].Wins + 1/2 * weights[0].Draws;
}
public static void function2_grad(double[] x, ref double func, double[] grad, object obj)
{
// this callback calculates xStar
// and its derivatives
Individual toBeOptimized = new Individual(x, "", 0);
func = xStarFunctionValue(toBeOptimized, population);
double[] gradCopy = xStarGradientValue(toBeOptimized, population);
for (int index = 0; index < grad.Length; index++)
grad[index] = gradCopy[index];
}
private static double[] xStarGradientValue(Individual xStar2, List<Individual> population)
{
Individual result = new Individual(tetha * (double)(-population.Count));
for (int index = 1; index < population.Count; index++)
{
double vValue = (new Individual(xStar2 - population[index])).DotProduct(tetha.Weights);
double gOfXY = (Math.Exp(vValue) - Math.Exp(-vValue)/(Math.Exp(vValue) + Math.Exp(-vValue)));
Individual newIndi = new Individual(tetha * gOfXY);
result = new Individual(result + newIndi);
}
return result.Weights;
}
private static double xStarFunctionValue(Individual xStar2, List<Individual> population)
{
Individual result = new Individual(xStar2 * (double)population.Count);
Individual sum = new Individual(new double[numberOfFeatures]);
double gOfXY = 0;
for (int index = 1; index < population.Count; index++)
{
sum = new Individual(sum + population[index]);
Individual newIndiv = new Individual(xStar2 - population[index]);
double localValue = newIndiv.DotProduct(tetha.Weights);
gOfXY += Math.Log(Math.Exp(localValue) + Math.Exp(-localValue));
}
result = new Individual(sum - result);
return result.DotProduct(tetha.Weights) + gOfXY;
}
private static double[] tethaGradientValue(Individual tetha2, List<Individual> population)
{
Individual result = new Individual(tetha2 / Math.Pow(sigma, 2)); ;
for (int row = 0; row < population.Count; row++)
{
for (int col = row; col < population.Count; col++)
{
if (row == col) continue;
Individual secondTerm = new Individual(new Individual(population[row] - population[col]) * (double)(numberOfGames - 2 * population[row].Labels[col]));
result = new Individual(result + secondTerm);
Individual difference = new Individual(population[row] - population[col]);
double vValue = difference.DotProduct(tetha2.Weights);
Individual gOfXY = new Individual(difference * ((Math.Exp(vValue) - Math.Exp(-vValue)) / (Math.Exp(vValue) + Math.Exp(-vValue))));
result = new Individual(result + gOfXY);
}
}
return result.Weights;
}
private static double tethaFunctionValue(Individual tetha2, List<Individual> population)
{
double result = 0;
result += Math.Pow(tetha2.EuclideanNorm(),2) / 2 * Math.Pow(sigma, 2);
for (int row = 0; row < population.Count; row++)
{
for (int col = row; col < population.Count; col++)
{
if (row == col) continue;
Individual newIndiv = new Individual(population[row] - population[col]);
double localValue = newIndiv.DotProduct(tetha2.Weights);
double gOfXY = Math.Log(Math.Exp(localValue) + Math.Exp(-localValue));
localValue = localValue * (double)(numberOfGames - 2 * population[row].Labels[col]) + (double)numberOfGames * gOfXY;
result += localValue;
}
}
return result;
}
static void Main(string[] args)
{
//DataReader data = new DataReader("Initial.txt");
//Individual weigthsPerceptron = data.GetPerceptronWeights(numberOfFeatures);
//List<Individual> players = data.GetPopulation(numberOfFeatures);
int gameCounter = 0;
double[] initialTetha = new double[numberOfFeatures];
double[] initialXStar = new double[numberOfFeatures];
for (int index = 0; index < numberOfFeatures; index++)
{
initialTetha[index] = rand.NextDouble();
initialXStar[index] = rand.NextDouble();
}
xStar = new Individual(initialXStar, "Star", initialPopulationSize + 1);
tetha = new Individual(initialTetha, "Tetha");
int iteration = 0;
DateTime maxDate = new DateTime(2011, 10, 24, 10, 30, 00);
do
{
if (iteration % 100 == 0)
{
population = initializePopulation();
FileStream file = new FileStream("Results.txt", FileMode.Append, FileAccess.Write);
StreamWriter sw = new StreamWriter(file);
sw.WriteLine("Played games {0}, iteration {1}", gameCounter, iteration);
sw.WriteLine("Tetha " + tetha.ToString());
sw.WriteLine("xStar " + xStar.ToString());
sw.Close();
file.Close();
}
for (int index = 0; index < population.Count; index++)
for (int index2 = 0; index2 < population.Count; index2++)
{
if (index == index2 || population[index].Labels[index2] != 0)
continue;
Individual[] weights = { population[index], population[index2] };
int classLabel = calculateLabel(weights);
gameCounter += (int)numberOfGames;
population[index].Labels[index2] = classLabel;
population[index2].Labels[index] = (int)numberOfGames - classLabel;
//population[index].Labels[index2] = rand.Next((int)numberOfGames);
//population[index2].Labels[index] = (int)numberOfGames - population[index].Labels[index2];
}
double epsg = 0.0000000001;
double epsf = 0;
double epsx = 0;
double stpmax = 0.1;
int maxits = 0;
alglib.mincgstate state;
alglib.mincgreport rep;
// tetha run
double[] newTetha = new double[numberOfFeatures];
for (int index = 0; index < newTetha.Length; index++)
newTetha[index] = tetha.Weights[index];
alglib.mincgcreate(newTetha, out state);
alglib.mincgsetcond(state, epsg, epsf, epsx, maxits);
alglib.mincgsetstpmax(state, stpmax);
alglib.mincgoptimize(state, function1_grad, null, null);
alglib.mincgresults(state, out newTetha, out rep);
for (int index = 0; index < newTetha.Length; index++)
tetha.Weights[index] = newTetha[index];
//xStarRun
double[] newXStar =new double[numberOfFeatures];
for (int index = 0; index < newTetha.Length; index++)
newXStar[index] = xStar.Weights[index];
alglib.mincgcreate(newXStar, out state);
alglib.mincgsetcond(state, epsg, epsf, epsx, maxits);
alglib.mincgsetstpmax(state, stpmax);
alglib.mincgoptimize(state, function2_grad, null, null);
alglib.mincgresults(state, out newXStar, out rep);
for (int index = 0; index < newTetha.Length; index++)
xStar.Weights[index] = newXStar[index];
xStar.ResetLables();
xStar.Normalize();
iteration++;
//for (int index = 0; index < population.Count; index++)
// population[index].ResetLables();
}
while (true);
}
