static void Main(string[] args)
{
Console.WriteLine(Directory.GetCurrentDirectory() + @"/demos/");
//string[] filePaths = Directory.GetFiles(Directory.GetCurrentDirectory() + @"/demos/", "*.dem");
ParserThread temp = new ParserThread(@"C:\Users\fredr\OneDrive\Dokument\KandidatArbetet\Demo Filer/", "*.dem");
Kmeans kMean = new Kmeans(temp.getPlayerData(), 5);
String tempString = "";
for (int x = 0; x < kMean.getCentroids().Length; x++)
{
tempString = "";
tempString += x + ": ";
for (int i = 0; i < kMean.getCentroids()[x].Length; i++)
{
tempString += kMean.getCentroids()[x][i] + " | ";
}
Console.WriteLine(tempString);
{
tempString = "";
tempString += x + ": ";
foreach (var value in kMean.getClusters()[x])
{
tempString += value + " | ";
}
Console.WriteLine(tempString);
}
}
Console.ReadKey();
}
static void Main(string[] args)
{
double[][] weights = new double[5][];
weights[0] = new double[Enum.GetNames(typeof(PlayerData.STAT)).Length * 2];
int t = 0;
foreach (var ping in Enum.GetNames(typeof(PlayerData.STAT)))
{
weights[0][t] = 1;
t++;
}
foreach (var ping in Enum.GetNames(typeof(PlayerData.STAT)))
{
weights[0][t] = 1;
t++;
}
weights[1] = new double[Enum.GetNames(typeof(PlayerData.STAT)).Length * 2];
int c = 0;
foreach (var value in System.IO.File.ReadAllText(@"..\ECO\Save Files\weights.txt").Split(" "))
{
if (value != "")
{
weights[1][c] = double.Parse(value);
}
c++;
}
string input = "";
string[] valuesInput;
while (input != "n" && input != "N")
{
t = 0;
foreach (var ping in Enum.GetNames(typeof(PlayerData.STAT)))
{
Console.WriteLine(t + ": " + "CT " + ping.ToString() + "W: " + weights[1][t]);
t++;
}
foreach (var ping in Enum.GetNames(typeof(PlayerData.STAT)))
{
Console.WriteLine(t + ": " + "T " + ping.ToString() + "W: " + weights[1][t]);
t++;
}
Console.WriteLine("To change a weight input the number of the weight and it's value, example 1 0,5... Input n/N when you are finished.");
input = Console.ReadLine();
valuesInput = input.Split(" ");
if (valuesInput.Length > 1)
{
weights[1][int.Parse(valuesInput[0])] = double.Parse(valuesInput[1]);
}
Console.Clear();
}
string weightsString = "";
foreach (var value in weights[1])
{
weightsString += value + " ";
}
System.IO.File.WriteAllText(@"..\ECO\Save Files\weights.txt", weightsString);
Console.WriteLine(Directory.GetCurrentDirectory() + @"/demos/");
//string[] filePaths = Directory.GetFiles(Directory.GetCurrentDirectory() + @"/Demo links/", "*.dem");
ParserThread temp = new ParserThread("", "*.dem");
System.IO.File.WriteAllLines(@"..\ECO\Save Files\matchresults.txt", temp.GetMatchResults().AsString().Split("\n"));
EA evolution = new EA(0.7, 0.7, (1 / (Enum.GetNames(typeof(PlayerData.STAT)).Length)), 0.3, 50, temp, 20);
evolution.RunGenerations(1, 8);
weights[2] = evolution.BestChild.Weights;
evolution = new EA(0.7, 0.7, (1 / (Enum.GetNames(typeof(PlayerData.STAT)).Length)), 0.3, 50, temp, 20);
evolution.RunGenerations(1, 16);
weights[3] = evolution.BestChild.Weights;
evolution = new EA(0.7, 0.7, (1 / (Enum.GetNames(typeof(PlayerData.STAT)).Length)), 0.3, 50, temp, 20);
evolution.RunGenerations(1, 32);
weights[4] = evolution.BestChild.Weights;
/*foreach(var key in pointsIn2D.Keys)
* {
* Console.WriteLine("X" + " " + "Y");
* foreach (var p in pointsIn2D[key])
* {
* Console.WriteLine(p[0] + " " + p[1]);
* }
* }*/
/*foreach (var row in System.IO.File.ReadAllLines(@"..\ECO\Save Files\matchresults.txt"))
* {
*
* long[] ctPlayers = new long[5];
* long[] tPlayers = new long[5];
* long[] results = new long[2];
*
* string[] matchResults = row.Split(" ");
* if (matchResults.Length > 10) {
* for (int x = 0; x < 10; x++)
* {
* if (x < 5)
* {
* ctPlayers[x] = long.Parse(matchResults[x]);
* }
* else
* {
* tPlayers[x - 5] = long.Parse(matchResults[x]);
* }
* }
* results[0] = long.Parse(matchResults[10]);
* results[1] = long.Parse(matchResults[11]);
* temp.GetMatchResults().AddMatchResult(ctPlayers, tPlayers, results);
* }
* }*/
string[] weightNames = { "NON", "SELF", "EAO8", "EAO16", "EAO32" };
foreach (int clusterAmount in clusterValues)
{
for (int testNum = 0; testNum < weights.Length; testNum++)
{
{ /*
* Kmeans kMean = new Kmeans(temp.getPlayerData(), numberOfClusters, weights[2]);
* String tempString = "";
* for (int x = 0; x < kMean.getCentroids().Length; x++)
* {
* tempString = "";
* tempString += x + ": ";
* for (int i = 0; i < kMean.getCentroids()[x].Length; i++)
* {
* tempString += kMean.getCentroids()[x][i] + " | ";
* }
* Console.WriteLine(tempString);
*
* {
* tempString = "";
* tempString += x + ": ";
* if (kMean.getClusters()[x] != null)
* foreach (var value in kMean.getClusters()[x])
* {
* tempString += value + " | ";
* }
* Console.WriteLine(tempString);
* }
*/
}
Kmeans kMean;
double winLossFittness;
double totalAmountOfPoints = 0;
MatchResults theMatchResults = new MatchResults();
kMean = new Kmeans(temp.getPlayerData(), clusterAmount, weights[testNum]);
theMatchResults = temp.GetMatchResults().Clone() as MatchResults;
theMatchResults.ConvertToClassesFromKmeans(kMean);
long[] wins = new long[clusterAmount];
long[] losses = new long[clusterAmount];
long[] row;
for (int rowNum = 0; rowNum < theMatchResults.MatchResultList.Count; rowNum++)
{
row = theMatchResults.MatchResultList[rowNum];
//foreach (var row in theMatchResults.MatchResultList)
//{
if (row.Length > 5)
{
for (int x = 0; x < 10; x++)
{
if (row[x] < clusterAmount)
{
if (x < 5)
{
if (row[10] > row[11])
{
wins[row[x]]++;
}
else
{
losses[row[x]]++;
}
}
else
{
if (row[10] < row[11])
{
wins[row[x]]++;
}
else
{
losses[row[x]]++;
}
}
}
}
}
}
winLossFittness = 0;
for (int x = 0; x < wins.Length; x++)
{
winLossFittness += (1 - Math.Abs(0.5 - ((double)wins[x] / (double)(wins[x] + losses[x])))) * (double)(wins[x] + losses[x]);
totalAmountOfPoints += (wins[x] + losses[x]);
}
winLossFittness = winLossFittness / (totalAmountOfPoints);
if (winLossFittness > 1)
{
winLossFittness = 1;
}
else if (winLossFittness == Double.NaN)
{
winLossFittness = 0;
}
//Console.WriteLine(winLossFittness);
double averageDistance = 0;
double[][] oldPos = kMean.getCentroids();
List <int> takenNumbers = new List <int>();
double lowestDis;
double thisDis;
int tempVal = 0;
double stabilityFittness = 0;
for (int x = 0; x < 2; x++)
{
foreach (double[] p in oldPos)
{
foreach (double[] cent in kMean.getCentroids())
{
averageDistance += Math.Sqrt(Math.Pow(p[0] - cent[0], 2) + Math.Pow(p[1] - cent[1], 2));
}
}
averageDistance = averageDistance / (oldPos.Length * oldPos.Length);
kMean = new Kmeans(temp.getPlayerData(), clusterAmount, weights[2]);
lowestDis = 999999;
foreach (double[] p in oldPos)
{
for (int y = 0; y < oldPos.Length; y++)
{
if (!takenNumbers.Contains(y))
{
thisDis = Math.Sqrt(Math.Pow(kMean.getCentroids()[y][0] - p[0], 2) + Math.Pow(kMean.getCentroids()[y][1] - p[1], 2));
if (thisDis < lowestDis)
{
lowestDis = thisDis;
tempVal = y;
}
}
}
takenNumbers.Add(tempVal);
stabilityFittness += 1 - (lowestDis / (averageDistance + lowestDis));
}
}
stabilityFittness = stabilityFittness / (oldPos.Length * 2);
if (stabilityFittness > 1)
{
stabilityFittness = 1;
}
Dictionary <int, List <double[]> > pointsIn2D = new Dictionary <int, List <double[]> >();
pointsIn2D = kMean.getClustersAs2DPoints();
//KMeansFunction
/*MatchResults matchResultsConversionHolder = temp.GetMatchResults().Clone() as MatchResults;
* matchResultsConversionHolder.ConvertToClassesFromKmeans(kMean);
* System.IO.File.WriteAllLines(@"..\ECO\Output\WriteLines.txt", temp.GetMatchResults().AsString().Split("\n"));
*
*
* long[] wins = new long[numberOfClusters];
* long[] losses = new long[numberOfClusters];
* foreach (var row in matchResultsConversionHolder.AsString().Split("\n"))
* {
* string[] matchResults = row.Split(" ");
* if (row != "" && matchResults.Length > 5) {
* for (int x = 0; x < 10; x++)
* {
* if(long.Parse(matchResults[x]) < numberOfClusters) {
* if (x < 5)
* {
* if (int.Parse(matchResults[10]) > int.Parse(matchResults[11]))
* {
* wins[long.Parse(matchResults[x])]++;
* }
* else
* {
* losses[long.Parse(matchResults[x])]++;
* }
* }
* else
* {
* if (int.Parse(matchResults[10]) < int.Parse(matchResults[11]))
* {
* wins[long.Parse(matchResults[x])]++;
* }
* else
* {
* losses[long.Parse(matchResults[x])]++;
* }
* }
* }
* }
* }
* }*/
double weightsFittness = 0;
foreach (double w in weights[testNum])
{
weightsFittness += w;
}
weightsFittness = weightsFittness / weights[testNum].Length;
double dd = dumbPrediction.calculateDumbPrediction(theMatchResults, wins, losses);
System.IO.File.WriteAllLines(@"..\ECO\Output\WriteLines" + "C" + clusterAmount + "W" + (testNum + 1) + weightNames[testNum] + "P" + Math.Round((dd * 100), 0) + ".txt", theMatchResults.AsString().Split("\n"));
UIGeneratorClass UI = new UIGeneratorClass(pointsIn2D, kMean.getCentroids(), temp.getStats(), wins, losses, "C" + clusterAmount + "W" + (testNum + 1) + weightNames[testNum] + "P" + Math.Round((dd * 100), 0), Math.Pow(winLossFittness, 3), Math.Pow(stabilityFittness, 2), weightsFittness, Math.Pow(winLossFittness, 3) * Math.Pow(stabilityFittness, 2) * weightsFittness, dd);
UI.generateHTML();
}
}
Console.ReadKey();
}