public static void TestBackpropagation()
{
var NN = NetworkCreator.Perceptron(2, 1, new [] { 20, 20, 20 });
var nums = 100000;
var data = new Dictionary <List <double>, List <double> >();
var rand = new Random();
var dif = 1000;
var shell = 500;
for (var i = 0; i < nums; i++)
{
var first = rand.Next(dif) - shell;
var second = rand.Next(dif) - shell;
data.Add(new List <double>()
{
first, second
},
new List <double>()
{
(first + second + 2 * shell) / 2d / dif,
});
}
BackpropagationAlgorithm.Teach(NN, data);
NN.Print();
for (var i = 0; i < 100; i++)
{
var first = rand.Next(dif) - shell;
var second = rand.Next(dif) - shell;
NN.Work(new List <double>()
{
first, second
});
var res = NN.GetResults()[0];
Console.WriteLine(first + " " + second + " . " + (first + second) + " " + (Math.Round((res * dif - shell) * 2)));
Console.ReadLine();
}
}
public static void TestGeneticAlgorithm()
{
var agents = new List <NeuralEnvironment>();
var population = 100;
var rand = new Random();
var dileme = new List <double>();
var options = 7;
for (var i = 0; i < options * options * 2; i++)
{
dileme.Add(rand.Next(11) / 10d);
}
var bacldileme = new List <double>();
for (var i = 0; i < options * options; i++)
{
bacldileme.Add(dileme[i * 2 + 1]);
bacldileme.Add(dileme[i * 2]);
}
for (var i = 0; i < options; i++)
{
for (var j = 0; j < options; j++)
{
Console.Write(Math.Round(dileme[2 * (i * options + j)] * 10)
+ " " + Math.Round(dileme[2 * (i * options + j) + 1] * 10));
if (j + 1 != options)
{
Console.Write(" | ");
}
}
Console.WriteLine();
}
Console.WriteLine();
Console.WriteLine();
for (var i = 0; i < population; i++)
{
agents.Add(NetworkCreator.Perceptron(8, options, new List <int>()
{
20
}));
}
var tournament = new Func <bool, Func <List <NeuralEnvironment>, List <double> > >(print => ((players) =>
{
var startElo = 0d;
var tours = 100;
var elo =
players.ToDictionary(player => player, player => startElo);
for (var game = 0; game < tours; game++)
{
//if(print)Console.WriteLine(game);
var pairing = new List <(NeuralEnvironment, NeuralEnvironment)>();
var list = players.OrderBy(p => - elo[p]).ToList();
for (var i = 0; i < players.Count / 2; i++)
{
var f = list[rand.Next(list.Count)];
list.Remove(f);
var s = list[rand.Next(list.Count)];
list.Remove(s);
pairing.Add((f, s));
}
Parallel.ForEach(pairing, pair =>
//foreach(var pair in pairing)
{
var(player1, player2) = pair;
player1.Work(dileme);
player2.Work(bacldileme);
var firstPlay = player1.GetMaxOutId();
var secondPlay = player2.GetMaxOutId();
//winwin
elo[player1] += dileme[firstPlay * 6 + secondPlay * 2];
elo[player2] += dileme[firstPlay * 6 + secondPlay * 2 + 1];
/*var E1 = 1 / (1 + Math.Pow(10, (elo[player2] - elo[player1]) / 400));
* var E2 = 1 / (1 + Math.Pow(10, (elo[player1] - elo[player2]) / 400));
* elo[player1] += 20 * (res[1] / points + (res[0] / draw) / 2d - E1);
* elo[player2] += 20 * (res[2] / points + (res[0] / draw) / 2d- E2);*/
//}
});
}
var bestPlayer = elo.OrderBy(p => p.Value).Last().Key;
if (print)
{
Console.WriteLine(Math.Round(elo[bestPlayer])
+ " "
+ Math.Round(elo.Select(p => p.Value).Sum() * 1d / elo.Count));
}
return(players.Select(p => elo[p]).ToList());
}));
var iterations = 1000;
for (var i = 0; i < iterations; i++)
{
agents = GeneticAlgorithm.Improve(
agents,
GeneticAlgorithm.RandomMerge,
tournament(false));
}
foreach (var agent in agents.Take(10))
{
agent.Work(dileme);
Console.Write(agent.GetMaxOutId() + " ");
agent.Work(bacldileme);
Console.WriteLine(agent.GetMaxOutId());
}
}